Somatic estrogen receptor α mutations that induce dimerization promote receptor activity and breast cancer proliferation.

Physiologic activation of estrogen receptor α (ERα) is mediated by estradiol (E2) binding in the ligand-binding pocket of the receptor, repositioning helix 12 (H12) to facilitate binding of coactivator proteins in the unoccupied coactivator binding groove. In breast cancer, activation of ERα is often observed through point mutations that lead to the same H12 repositioning in the absence of E2. Through expanded genetic sequencing of breast cancer patients, we identified a collection of mutations located far from H12 but nonetheless capable of promoting E2-independent transcription and breast cancer cell growth. Using machine learning and computational structure analyses, this set of mutants was inferred to act distinctly from the H12-repositioning mutants and instead was associated with conformational changes across the ERα dimer interface. Through both in vitro and in-cell assays of full-length ERα protein and isolated ligand-binding domain, we found that these mutants promoted ERα dimerization, stability, and nuclear localization. Point mutations that selectively disrupted dimerization abrogated E2-independent transcriptional activity of these dimer-promoting mutants. The results reveal a distinct mechanism for activation of ERα function through enforced receptor dimerization and suggest dimer disruption as a potential therapeutic strategy to treat ER-dependent cancers.

Amifostine inhibits acrylamide-induced hepatotoxicity by inhibiting oxidative stress and apoptosis.

Objectives: Acrylamide (ACR) is a toxic chemical agent that can induce hepatotoxicity through different mechanisms including oxidative stress and apoptosis. Amifostine is an important hepatoprotective and anti-oxidant compound. In this research, the hepatoprotective effect of amifostine on ACR-induced hepatotoxicity in rats has been investigated. Materials and Methods: Male Wistar rats were randomly divided into 7 groups, including: 1. Control group, 2. ACR (50 mg/kg, 11 days, IP), 3-5. ACR+ amifostine (25, 50, 100 mg/kg, 11 days, IP), 6. ACR+ N-acetyl cysteine (NAC) (200 mg/kg, 11 days, IP), and 7. Amifostine (100 mg/kg, 11 days, IP). At the end of the injection period, animals' liver samples were collected to determine the content of glutathione (GSH), malondialdehyde (MDA), and apoptotic proteins (B-cell lymphoma 2 (Bcl2), Bcl-2-associated X protein (Bax), and cleaved caspase-3. Serum samples were also collected to measure alanine transaminase (ALT) and aspartate transaminase (AST) levels. Results: Administration of ACR increased MDA, Bax/Bcl2 ratio, cleaved caspase-3, ALT, and AST levels, and decreased GSH content compared with the control group. The administration of amifostine with ACR decreased MDA, Bax/Bcl2 ratio, cleaved caspase-3, ALT, and AST levels, and increased GSH content compared with the ACR group. Receiving NAC along with ACR reversed the alterations induced by ACR. Conclusion: This study shows that pretreatment with amifostine can reduce ACR-induced toxicity in the liver tissue of rats. Since oxidative stress is one of the most important mechanisms in ACR toxicity, amifostine probably reduces the toxicity of ACR by increasing the anti-oxidant and anti-apoptotic capacity of the hepatic cells.

Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts.

Predicting compound-protein affinity is beneficial for accelerating drug discovery. Doing so without the often-unavailable structure data is gaining interest. However, recent progress in structure-free affinity prediction, made by machine learning, focuses on accuracy but leaves much to be desired for interpretability. Defining intermolecular contacts underlying affinities as a vehicle for interpretability; our large-scale interpretability assessment finds previously used attention mechanisms inadequate. We thus formulate a hierarchical multiobjective learning problem, where predicted contacts form the basis for predicted affinities. We solve the problem by embedding protein sequences (by hierarchical recurrent neural networks) and compound graphs (by graph neural networks) with joint attentions between protein residues and compound atoms. We further introduce three methodological advances to enhance interpretability: (1) structure-aware regularization of attentions using protein sequence-predicted solvent exposure and residue-residue contact maps; (2) supervision of attentions using known intermolecular contacts in training data; and (3) an intrinsically explainable architecture where atomic-level contacts or "relations" lead to molecular-level affinity prediction. The first two and all three advances result in DeepAffinity+ and DeepRelations, respectively. Our methods show generalizability in affinity prediction for molecules that are new and dissimilar to training examples. Moreover, they show superior interpretability compared to state-of-the-art interpretable methods: with similar or better affinity prediction, they boost the AUPRC of contact prediction by around 33-, 35-, 10-, and 9-fold for the default test, new-compound, new-protein, and both-new sets, respectively. We further demonstrate their potential utilities in contact-assisted docking, structure-free binding site prediction, and structure-activity relationship studies without docking. Our study represents the first model development and systematic model assessment dedicated to interpretable machine learning for structure-free compound-protein affinity prediction.

Directionally dependent multi-view clustering using copula model.

Recent developments in high-throughput methods have resulted in the collection of high-dimensional data types from multiple sources and technologies that measure distinct yet complementary information. Integrated clustering of such multiple data types or multi-view clustering is critical for revealing pathological insights. However, multi-view clustering is challenging due to the complex dependence structure between multiple data types, including directional dependency. Specifically, genomics data types have pre-specified directional dependencies known as the central dogma that describes the process of information flow from DNA to messenger RNA (mRNA) and then from mRNA to protein. Most of the existing multi-view clustering approaches assume an independent structure or pair-wise (non-directional) dependence between data types, thereby ignoring their directional relationship. Motivated by this, we propose a biology-inspired Bayesian integrated multi-view clustering model that uses an asymmetric copula to accommodate the directional dependencies between the data types. Via extensive simulation experiments, we demonstrate the negative impact of ignoring directional dependency on clustering performance. We also present an application of our model to a real-world dataset of breast cancer tumor samples collected from The Cancer Genome Altas program and provide comparative results.

De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks.

Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal underlying sequence-structure relationships and design protein sequences for an arbitrary, potentially novel, structural fold? In response to the question, we have developed novel deep generative models, namely, semisupervised gcWGAN (guided, conditional, Wasserstein Generative Adversarial Networks). To overcome training difficulties and improve design qualities, we build our models on conditional Wasserstein GAN (WGAN) that uses Wasserstein distance in the loss function. Our major contributions include (1) constructing a low-dimensional and generalizable representation of the fold space for the conditional input, (2) developing an ultrafast sequence-to-fold predictor (or oracle) and incorporating its feedback into WGAN as a loss to guide model training, and (3) exploiting sequence data with and without paired structures to enable a semisupervised training strategy. Assessed by the oracle over 100 novel folds not in the training set, gcWGAN generates more successful designs and covers 3.5 times more target folds compared to a competing data-driven method (cVAE). Assessed by sequence- and structure-based predictors, gcWGAN designs are physically and biologically sound. Assessed by a structure predictor over representative novel folds, including one not even part of basis folds, gcWGAN designs have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE. The ultrafast data-driven model is further shown to boost the success of a principle-driven de novo method (RosettaDesign), through generating design seeds and tailoring design space. In conclusion, gcWGAN explores uncharted sequence space to design proteins by learning generalizable principles from current sequence-structure data. Data, source codes, and trained models are available at https://github.com/Shen-Lab/gcWGAN.

Network-principled deep generative models for designing drug combinations as graph sets.

MOTIVATION: Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. RESULTS: We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene-gene, gene-disease and disease-disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes' representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. AVAILABILITY AND IMPLEMENTATION: https://github.com/Shen-Lab/Drug-Combo-Generator. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge.

Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far-UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant (ΔΔGH2O) . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the ΔΔGH2O value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.

Predicting pathogenicity of missense variants with weakly supervised regression.

Quickly growing genetic variation data of unknown clinical significance demand computational methods that can reliably predict clinical phenotypes and deeply unravel molecular mechanisms. On the platform enabled by the Critical Assessment of Genome Interpretation (CAGI), we develop a novel "weakly supervised" regression (WSR) model that not only predicts precise clinical significance (probability of pathogenicity) from inexact training annotations (class of pathogenicity) but also infers underlying molecular mechanisms in a variant-specific manner. Compared to multiclass logistic regression, a representative multiclass classifier, our kernelized WSR improves the performance for the ENIGMA Challenge set from 0.72 to 0.97 in binary area under the receiver operating characteristic curve (AUC) and from 0.64 to 0.80 in ordinal multiclass AUC. WSR model interpretation and protein structural interpretation reach consensus in corroborating the most probable molecular mechanisms by which some pathogenic BRCA1 variants confer clinical significance, namely metal-binding disruption for p.C44F and p.C47Y, protein-binding disruption for p.M18T, and structure destabilization for p.S1715N.

Engineering a HER2-specific antibody-drug conjugate to increase lysosomal delivery and therapeutic efficacy.

We improve the potency of antibody-drug conjugates (ADCs) containing the human epidermal growth factor receptor 2 (HER2)-specific antibody pertuzumab by substantially reducing their affinity for HER2 at acidic endosomal pH relative to near neutral pH. These engineered pertuzumab variants show increased lysosomal delivery and cytotoxicity towards tumor cells expressing intermediate HER2 levels. In HER2int xenograft tumor models in mice, the variants show higher therapeutic efficacy than the parent ADC and a clinically approved HER2-specific ADC.

DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks.

MOTIVATION: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability. RESULTS: We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead. AVAILABILITY AND IMPLEMENTATION: Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The SERM/SERD bazedoxifene disrupts ESR1 helix 12 to overcome acquired hormone resistance in breast cancer cells.

Acquired resistance to endocrine therapy remains a significant clinical burden for breast cancer patients. Somatic mutations in the ESR1 (estrogen receptor alpha (ERα)) gene ligand-binding domain (LBD) represent a recognized mechanism of acquired resistance. Antiestrogens with improved efficacy versus tamoxifen might overcome the resistant phenotype in ER +breast cancers. Bazedoxifene (BZA) is a potent antiestrogen that is clinically approved for use in hormone replacement therapies. We found that BZA possesses improved inhibitory potency against the Y537S and D538G ERα mutants compared to tamoxifen and has additional inhibitory activity in combination with the CDK4/6 inhibitor palbociclib. In addition, comprehensive biophysical and structural biology studies show BZA's selective estrogen receptor degrading (SERD) properties that override the stabilizing effects of the Y537S and D538G ERα mutations.

iCFN: an efficient exact algorithm for multistate protein design.

Motivation: Multistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an enormous challenge to exact algorithms that guarantee the optimal solutions and enable a direct test of mechanistic hypotheses behind models. However, efficient exact algorithms are lacking for multistate protein design. Results: We have developed an efficient exact algorithm called interconnected cost function networks (iCFN) for multistate protein design. Its generic formulation allows for a wide array of applications such as stability, affinity and specificity designs while addressing concerns such as global flexibility of protein backbones. iCFN treats each substate design as a weighted constraint satisfaction problem (WCSP) modeled through a CFN; and it solves the coupled WCSPs using novel bounds and a depth-first branch-and-bound search over a tree structure of sequences, substates, and conformations. When iCFN is applied to specificity design of a T-cell receptor, a problem of unprecedented size to exact methods, it drastically reduces search space and running time to make the problem tractable. Moreover, iCFN generates experimentally-agreeing receptor designs with improved accuracy compared with state-of-the-art methods, highlights the importance of modeling backbone flexibility in protein design, and reveals molecular mechanisms underlying binding specificity. Availability and implementation: https://shen-lab.github.io/software/iCFN. Supplementary information: Supplementary data are available at Bioinformatics online.

Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas.

DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ∼20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy.

Association between ELMO1 gene polymorphisms and diabetic nephropathy in an Iranian population.

BACKGROUND: Diabetic nephropathy (DN) is one of the leading causes of death in patients with type 2 diabetes mellitus (T2DM). Several genome-wide association studies have introduced Engulfment and Cell Motility 1 (ELMO1) as a candidate gene which is associated with DN. This study assessed the association of ELMO1 gene polymorphisms with DN in order to investigate the effects of ELMO1 gene on susceptibility to DN in an Iranian population. METHODS: In the present study, 100 patients with T2DM, 100 patients with DN and 100 healthy subjects who were matched for sex were selected. Allele and genotype frequencies were determined by Tetra-ARMS PCR technique. In all groups, levels of FBS, creatinine, urea, HbA1C, urine levels of albumin creatinine ratio and glomerular filtration rate were measured. RESULTS: A statistically significant association was shown between G allele of rs741301 (odds ratio (OR) = 1.7 [95 % CI 1.17-2.63]; p value = 0.005), and GG genotypes of rs741301 (OR = 2.5 [95 % CI 1.2-5.4]; p value = 0.01) and DN. A significant association was not detected between allelic and genotypic frequencies of rs1345365 and DN. Linkage Disequilibrium (LD) between two variants was weak (D' = 0.11, r2 = 0.008). rs1345365A/rs741301A haplotypes were more frequent in patients with T2DM as compared to DN (OR = 0.5 [95 % CI 0.3-0.7]; p value = 0.0006). Also, genotypes of variant rs741301 in all subjects had significant difference with respect to the mean of ACR (p Value < 0.05). CONCLUSION: This study first investigated the association of ELMO1 gene polymorphisms (rs741301) with DN in an Iranian population, supporting its key role as a candidate gene in the susceptibility to DN.

Preparation and characterization of MWCNT-graft-PCA-Pt electrode fabricated by electrospray deposition method for proton exchange membrane fuel cell.

In this paper, the development and application of a novel carbon nanotube/Polycitric acid (MWCNT-graft-PCA-Pt) nanocomposite as an efficient nanocatalyst for PEM fuel cell is reported. Covalent attachment to PCA agents is the main method for the modification of CNTs with polymers. By this method electrocatalysts with a narrow particle-size distribution and good dispersion have been produced. Carbon nanotube (CNT) film electrodes have been fabricated by a novel process involving the electrostatic spray deposition (ESD) of a CNT solution. The CNT film electrodes have shown well-entangled and interconnected porous structures with good adherence to the substrate. Cyclic voltammograms (CV) of catalysts using the spongy thin layer electrode technique were obtained for the catalysts surface at evaluation and for Methanol Oxidation reaction (MOR). CV results have demonstrated that the current density and MOR activity of the MWCNT-graft-PCA-Pt is respectively higher than of the MWCNT-Pt nanocatalyst.