Catalytic Water Co-Existing with a Product Peptide in the Active Site of HIV-1 Protease Revealed by X-Ray Structure Analysis

BACKGROUND: It is known that HIV-1 protease is an important target for design of antiviral compounds in the treatment of Acquired Immuno Deficiency Syndrome (AIDS). In this context, understanding the catalytic mechanism of the enzyme is of crucial importance as transition state structure directs inhibitor design. Most mechanistic proposals invoke nucleophilic attack on the scissile peptide bond by a water molecule. But such a water molecule coexisting with any ligand in the active site has not been found so far in the crystal structures. PRINCIPAL FINDINGS: We report here the first observation of the coexistence in the active site, of a water molecule WAT1, along with the carboxyl terminal product (Q product) peptide. The product peptide has been generated in situ through cleavage of the full-length substrate. The N-terminal product (P product) has diffused out and is replaced by a set of water molecules while the Q product is still held in the active site through hydrogen bonds. The position of WAT1, which hydrogen bonds to both the catalytic aspartates, is different from when there is no substrate bound in the active site. We propose WAT1 to be the position from where catalytic water attacks the scissile peptide bond. Comparison of structures of HIV-1 protease complexed with the same oligopeptide substrate, but at pH 2.0 and at pH 7.0 shows interesting changes in the conformation and hydrogen bonding interactions from the catalytic aspartates. CONCLUSIONS/SIGNIFICANCE: The structure is suggestive of the repositioning, during substrate binding, of the catalytic water for activation and subsequent nucleophilic attack. The structure could be a snap shot of the enzyme active site primed for the next round of catalysis. This structure further suggests that to achieve the goal of designing inhibitors mimicking the transition-state, the hydrogen-bonding pattern between WAT1 and the enzyme should be replicated

Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Cancer is a severe condition of uncontrolled cell division that results in a tumor formation that spreads to other tissues of the body. Therefore, the development of new medication and treatment methods for this is in demand. Classification of microarray data plays a vital role in handling such situations. The relevant gene selection is an important step for the classification of microarray data. This work presents gene encoder, an unsupervised two-stage feature selection technique for the cancer samples’ classification. The first stage aggregates three filter methods, namely principal component analysis, correlation, and spectral-based feature selection techniques. Next, the genetic algorithm is used, which evaluates the chromosome utilizing the autoencoder-based clustering. The resultant feature subset is used for the classification task. Three classifiers, namely support vector machine, k-nearest neighbors, and random forest, are used in this work to avoid the dependency on any one classifier. Six benchmark gene expression datasets are used for the performance evaluation, and a comparison is made with four state-of-the-art related algorithms. Three sets of experiments are carried out to evaluate the proposed method. These experiments are for the evaluation of the selected features based on sample-based clustering, adjusting optimal parameters, and for selecting better performing classifier. The comparison is based on accuracy, recall, false positive rate, precision, F-measure, and entropy. The obtained results suggest better performance of the current proposal

Effective combination therapies in preclinical endocrine resistant breast cancer models harboring ER mutations.

Although endocrine therapy is successfully used to treat patients with estrogen receptor (ER) positive breast cancer, a substantial proportion of this population will relapse. Several mechanisms of acquired resistance have been described including activation of the mTOR pathway, increased activity of CDK4 and activating mutations in ER. Using a patient derived xenograft model harboring a common activating ER ligand binding domain mutation (D538G), we evaluated several combinatorial strategies using the selective estrogen receptor degrader (SERD) fulvestrant in combination with chromatin modifying agents, and CDK4/6 and mTOR inhibitors. In this model, fulvestrant binds WT and MT ER, reduces ER protein levels, and downregulated ER target gene expression. Addition of JQ1 or vorinostat to fulvestrant resulted in tumor regression (41% and 22% regression, respectively) though no efficacy was seen when either agent was given alone. Interestingly, although the CDK4/6 inhibitor palbociclib and mTOR inhibitor everolimus were efficacious as monotherapies, long-term delayed tumor growth was only observed when co-administered with fulvestrant. This observation was consistent with a greater inhibition of compensatory signaling when palbociclib and everolimus were co-dosed with fulvestrant. The addition of fulvestrant to JQ1, vorinostat, everolimus and palbociclib also significantly reduced lung metastatic burden as compared to monotherapy. The combination potential of fulvestrant with palbociclib or everolimus were confirmed in an MCF7 CRISPR model harboring the Y537S ER activating mutation. Taken together, these data suggest that fulvestrant may have an important role in the treatment of ER positive breast cancer with acquired ER mutations

Elacestrant demonstrates strong anti-estrogenic activity in PDX models of estrogen-receptor positive endocrine-resistant and fulvestrant-resistant breast cancer.

The selective oestrogen receptor (ER) degrader (SERD), fulvestrant, is limited in its use for the treatment of breast cancer (BC) by its poor oral bioavailability. Comparison of the orally bioavailable investigational SERD elacestrant, versus fulvestrant, demonstrates both drugs impact tumour growth of ER+ patient-derived xenograft models harbouring several ESR1 mutations but that elacestrant is active after acquired resistance to fulvestrant. In cell line models of endocrine sensitive and resistant breast cancer both drugs impact the ER-cistrome, ER-interactome and transcription of oestrogen-regulated genes similarly, confirming the anti-oestrogenic activity of elacestrant. The addition of elacestrant to CDK4/6 inhibitors enhances the antiproliferative effect compared to monotherapy. Furthermore, elacestrant inhibits the growth of palbociclib-resistant cells. Lastly, resistance to elacestrant involves Type-I and Type-II receptor tyrosine kinases which are amenable to therapeutic targeting. Our data support the wider clinical testing of elacestrant