Interdomain twists of human thymidine phosphorylase and its active-inactive conformations: Binding of 5-FU and its analogues to human thymidine phosphorylase versus dihydropyrimidine dehydrogenase

Neighborhood-based inference and restricted Boltzmann machine for small molecule-miRNA associations prediction

Background

A growing number of experiments have shown that microRNAs (miRNAs) can be used as target of small molecules (SMs) to regulate gene expression for treating diseases. Therefore, identifying SM-related miRNAs is helpful for the treatment of diseases in the domain of medical investigation.

Methods

This article presents a new computational model, called NIRBMSMMA (neighborhood-based inference (NI) and restricted Boltzmann machine (RBM)), which we developed to identify potential small molecule-miRNA associations (NIRBMSMMA). First, grounded on known SM-miRNAs associations, SM similarity and miRNA similarity, NI was used to predict score of an unknown SM-miRNA pair by reckoning the sum of known associations between neighbors of the SM (miRNA) and the miRNA (SM). Second, utilizing a two-layered generative stochastic artificial neural network, RBM was used to predict SM-miRNA association by learning potential probability distribution from known SM-miRNA associations. At last, an ensemble learning model was conducted to combine NI and RBM for identifying potential SM-miRNA associations.

Results

Furthermore, we conducted global leave one out cross validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation to assess performance of NIRBMSMMA based on three datasets. Results showed that NIRBMSMMA obtained areas under the curve (AUC) of 0.9912, 0.9875, 0.8376 and 0.9898 ± 0.0009 under global LOOCV, miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation based on dataset 1, respectively. For dataset 2, the AUCs are 0.8645, 0.8720, 0.7066 and 0.8547 ± 0.0046 in turn. For dataset 3, the AUCs are 0.9884, 0.9802, 0.8239 and 0.9870 ± 0.0015 in turn. Also, we conducted case studies to further assess the predictive performance of NIRBMSMMA. These results illustrated the proposed model is a useful tool in predicting potential SM-miRNA associations.

Biased Borate Esterification during Nucleoside Phosphorylase-Catalyzed Reactions: Apparent Equilibrium Shifts and Kinetic Implications

Biocatalytic nucleoside (trans-)glycosylations catalyzed by nucleoside phosphorylases have evolved into a practical and convenient approach to the preparation of modified nucleosides, which are important pharmaceuticals for the treatment of various cancers and viral infections. However, the obtained yields in these reactions are generally determined exclusively by the innate thermodynamic properties of the nucleosides involved, hampering the biocatalytic access to many sought-after target nucleosides. We herein report an additional means for reaction engineering of these systems. We show how apparent equilibrium shifts in phosphorolysis and glycosylation reactions can be effected through entropically driven, biased esterification of nucleosides and ribosyl phosphates with inorganic borate. Our multifaceted analysis further describes the kinetic implications of this in situ reactant esterification for a model phosphorylase.

Energy decomposition and waterswapping analysis to investigate the SNP associated DPD mediated 5-FU resistance

5-fluorouracil is an essential component of systemic chemotherapy for colon, breast, head, and neck cancer patients. However, tumoral overexpression of the dihydropyrimidine dehydrogenase has rendered 5-FU clinically ineffective by inactivating it to 5'-6'-dihydro fluorouracil. The responses to 5-FU in terms of efficacy and toxicity greatly differ depending upon the population group, because of variability in the DPD activity levels. In the current study, key active site amino acids involved in the 5-FU inactivation were investigated by modelling the 3D structure of human DPD in a complex with 5-FU. The identified amino acids were analyzed for their possible missense mutations available in dbSNP database. Out of 12 missense SNPs, four were validated either by sequencing in the 1000 Genomes project or frequency/genotype data. The recorded validated missense SNPs were further considered to analyze the effect of their respective alterations on 5-FU binding. Overall findings suggested that population bearing the Glu611Val DPD mutation (rs762523739) is highly vulnerable to 5-FU resistance. From the docking, electrostatic complementarity, dynamics, and energy decomposition analyses it was found that the above mutation showed superior scores than the wild DPD -5FU complex. Therefore, prescribing prodrug NUC-3373 or DPD inhibitors (Gimeracil/3-Cyano-2,6-Dihydroxypyridines) as adjuvant therapy may overcome the 5-FU resistance.

Dihydropyrimidine Dehydrogenase-Mediated Resistance to 5-Fluorouracil: Mechanistic Investigation and Solution

5-Fluorouracil (5-FU) is one of the most widely used chemotherapeutics for the treatment of cancers associated with the aerodigestive tract, breast, and colorectal system. The efficacy of 5-FU is majorly affected by dihydropyrimidine dehydrogenase (DPD) as it degrades more than 80% of administered 5-FU into an inactive metabolite, dihydrofluorouracil. Herein we discuss the molecular mechanism of this inactivation by analyzing the interaction pattern and electrostatic complementarity of the DPD-5-FU complex. The basis of DPD overexpression in cancer cell lines due to significantly distinct levels of the miRNAs (miR-134, miR-27b, and miR-27a) compared to normal cells has also been outlined. Additionally, some kinases including sphingosine kinase 2 (SphK2) have been reported to correlate with DPD expression. Currently, to address this problem various strategies are reported in the literature, including 5-FU analogues (bypass the DPD-mediated inactivation), DPD downregulators (regulate the DPD expression levels in tumors), inhibitors (as promising adjuvants), and formulation development loaded with 5-FU (liposomes, nanoparticles, nanogels, etc.), which are briefly discussed in this Review.

Force Field Parameters for Fe2+4S2-4 Clusters of Dihydropyrimidine Dehydrogenase, the 5-Fluorouracil Cancer Drug Deactivation Protein: A Step towards In Silico Pharmacogenomics Studies

The dimeric dihydropyrimidine dehydrogenase (DPD), metalloenzyme, an adjunct anti-cancer drug target, contains highly specialized 4 × Fe²⁺₄S²⁻₄ clusters per chain. These clusters facilitate the catalysis of the rate-limiting step in the pyrimidine degradation pathway through a harmonized electron transfer cascade that triggers a redox catabolic reaction. In the process, the bulk of the administered 5-fluorouracil (5-FU) cancer drug is inactivated, while a small proportion is activated to nucleic acid antimetabolites. The occurrence of missense mutations in DPD protein within the general population, including those of African descent, has adverse toxicity effects due to altered 5-FU metabolism. Thus, deciphering mutation effects on protein structure and function is vital, especially for precision medicine purposes. We previously proposed combining molecular dynamics (MD) and dynamic residue network (DRN) analysis to decipher the molecular mechanisms of missense mutations in other proteins. However, the presence of Fe²⁺₄S²⁻₄ clusters in DPD poses a challenge for such in silico studies. The existing AMBER force field parameters cannot accurately describe the Fe²⁺ center coordination exhibited by this enzyme. Therefore, this study aimed to derive AMBER force field parameters for DPD enzyme Fe²⁺ centers, using the original Seminario method and the collation features Visual Force Field Derivation Toolkit as a supportive approach. All-atom MD simulations were performed to validate the results. Both approaches generated similar force field parameters, which accurately described the human DPD protein Fe²⁺₄S²⁻₄ cluster architecture. This information is crucial and opens new avenues for in silico cancer pharmacogenomics and drug discovery related research on 5-FU drug efficacy and toxicity issues.

Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review

Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these in-silico tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.

C-terminal Tail of β-Tubulin and its Role in the Alterations of Dynein Binding Mode

Dynein is a cytoskeletal molecular motor protein that moves along the microtubule (MT) and transports various cellular cargos during its movement. Using standard Molecular Dynamics (MD) simulation, Principle Component Analysis (PCA), and Normal Mode Analysis (NMA) methods, this investigation studied large-scale movements and local interactions of dynein's Microtubule Binding Domain (MTBD) when bound to tubulin heterodimer subunits. Examination of the interactions between the MTBD segments, and their adjustments in terms of intra- and intermolecular distances at the interfacial area with tubulin heterodimer, particularly at α-H16, β-H18, and β-tubulin C-terminal tail (CTT), was the main focus of this study. The specific intramolecular interactions, electrostatic forces, and the salt bridge residue pairs were shown to be the dominating factors in orchestrating movements of the MTBD and MT interfacial segments in the dynein's low-high-affinity binding modes. Important interactions included β-Glu447 and β-Glu449 (CTT) with Arg3469 (MTBD-H6), Lys3472 (MTBD-H6-H7 loop) and Lys3479 (MTBD-H7); β-Glu449 with Lys3384 (MTBD-H8), Lys3386 and His3387 (MTBD-H1). The structural and precise position, orientation, and functional effects of the CTTs on the MT-MTBD, within reasonable cut-off distance for non-bonding interactions and under physiological conditions, are unavailable from previous studies. The absence of the residues in the highly flexible MT-CTTs in the experimentally solved structures is perhaps in some cases due to insufficient data from density maps, but these segments are crucial in protein binding. The presented work contributes to the information useful for the MT-MTBD structure refinement.

New DPYD variants causing DPD deficiency in patients treated with fluoropyrimidine

PURPOSE

Several clinical guidelines recommend genetic screening of DPYD, including coverage of the variants c.1905 + 1G>A(DPYD*2A), c.1679T>G(DPYD*13), c.2846A>T, and c.1129-5923C>G, before initiating treatment with fluoropyrimidines. However, this screening is often inadequate at predicting the occurrence of severe fluoropyrimidine-induced toxicity in patients.

METHODS

Using a complementary approach combining whole DPYD exome sequencing and in silico and structural analysis, as well as phenotyping of DPD by measuring uracilemia (U), dihydrouracilemia (UH₂), and the UH₂/U ratio in plasma, we were able to characterize and interpret DPYD variants in 28 patients with severe fluoropyrimidine-induced toxicity after negative screening.

RESULTS

Twenty-five out of 28 patients (90%) had at least 1 variant in the DPYD coding sequence, and 42% of the variants (6/14) were classified as potentially deleterious by at least 2 of the following algorithms: SIFT, Poly-Phen-2, and DPYD varifier. We identified two very rare deleterious mutations, namely, c.2087G>A (p.R696H) and c.2324T>G (p.L775W). We were able to demonstrate partial DPD deficiency, as measured by the UH₂/U ratio in a patient carrying the variant p.L775W for the first time.

CONCLUSION

Whole exon sequencing of DPYD in patients with suspicion of partial DPD deficiency can help to identify rare or new variants that lead to enzyme inactivation. Combining different techniques can yield abundant information without increasing workload and cost burden, thus making it a useful approach for implementation in patient care.