A harmonized resource of integrated prostate cancer clinical, -omic, and signature features.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data.

BACKGROUND: Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005-0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection. RESULT: Here, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs (< 2 Mbp) were detected. There was also significant variability in their ability to identify CNVs in the sex chromosomes. Overall, BIC-seq2 was found to be the best method in terms of statistical performance. However, its significant drawback was by far the slowest runtime among the methods (> 3 h) compared with FREEC (~ 3 min), which we considered the second-best method. CONCLUSIONS: Our comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.

Predicted Hotspot Residues Involved in Allosteric Signal Transmission in Pro-Apoptotic Peptide-Mcl1 Complexes.

Mcl1 is a primary member of the Bcl-2 family-anti-apoptotic proteins (AAP)-that is overexpressed in several cancer pathologies. The apoptotic regulation is mediated through the binding of pro-apoptotic peptides (PAPs) (e.g., Bak and Bid) at the canonical hydrophobic binding groove (CBG) of Mcl1. Although all PAPs form amphipathic α-helices, their amino acid sequences vary to different degree. This sequence variation exhibits a central role in the binding partner selectivity towards different AAPs. Thus, constructing a novel peptide or small organic molecule with the ability to mimic the natural regulatory process of PAP is essential to inhibit various AAPs. Previously reported experimental binding free energies (BFEs) were utilized in the current investigation aimed to understand the mechanistic basis of different PAPs targeted to mMcl1. Molecular dynamics (MD) simulations used to estimate BFEs between mMcl1-PAP complexes using Molecular Mechanics-Generalized Born Solvent Accessible (MMGBSA) approach with multiple parameters. Predicted BFE values showed an excellent agreement with the experiment (R2 = 0.92). The van-der Waals (ΔGvdw) and electrostatic (ΔGele) energy terms found to be the main energy components that drive heterodimerization of mMcl1-PAP complexes. Finally, the dynamic network analysis predicted the allosteric signal transmission pathway involves more favorable energy contributing residues. In total, the results obtained from the current investigation may provide valuable insights for the synthesis of a novel peptide or small organic inhibitor targeting Mcl1.

Unraveling the molecular mechanism of benzothiophene and benzofuran scaffold-merged compounds binding to anti-apoptotic Myeloid cell leukemia 1.

Myeloid cell leukemia 1 (Mcl1), is an antiapoptotic member of the Bcl-2 family proteins, has gained considerable importance due to its overexpression activity prevents the oncogenic cells to undergo apoptosis. This overexpression activity of Mcl1 eventually develops strong resistance to a wide variety of anticancer agents. Therefore, designing novel inhibitors with potentials to elicit higher binding affinity and specificity to inhibit Mcl1 activity is of greater importance. Thus, Mcl1 acts as an attractive cancer target. Despite recent experimental advancement in the identification and characterization of benzothiophene and benzofuran scaffold-merged compounds, the molecular mechanisms of their binding to Mcl1 are yet to be explored. The current study demonstrates an integrated approach - pharmacophore-based 3D-QSAR, docking, molecular dynamics (MD) simulation and free-energy estimation - to access the precise and comprehensive effects of current inhibitors targeting Mcl1 together with its known activity values. The pharmacophore - ANRRR.240 - based 3D-QSAR model from the current study provided high confidence (R2=0.9154, Q2=0.8736 and RMSE = 0.3533) values. Furthermore, the docking correctly predicted the binding mode of highly active compound 42. Additionally, the MD simulation for docked complex under explicit-solvent conditions together with free-energy estimation exhibited stable interaction and binding strength over the time period. Also, the decomposition analysis revealed potential energy contributing residues - M231, M250, V253, R265, L267 and F270 - to the complex stability. Overall, the current investigation might serve as a valuable insight, either to (i) improve the binding affinity of the current compounds or (ii) discover new generation anticancer agents that can effectively downregulate Mcl1 activity. Communicated by Ramaswamy H. Sarma.

Prediction of Hot Spots at Myeloid Cell Leukemia-1-Inhibitor Interface Using Energy Estimation and Alanine Scanning Mutagenesis.

Myeloid cell leukemia 1 (Mcl1) is an antiapoptotic protein that plays central role in apoptosis regulation. Also, Mcl1 has the potency to resist apoptotic cues resulting in up-regulation and cancer cell protection. A molecular probe that has the potential to specifically target Mcl1 and thereby provoke its down-regulatory activity is very essential. The aim of the current study is to probe the internal conformational dynamics of protein motions and potential binding mechanism in response to a series of picomolar range Mcl1 inhibitors using explicit-solvent molecular dynamics (MD) simulations. Subsequently, domain cross-correlation and principal component analysis was performed on the snapshots obtained from the MD simulations. Our results showed significant differences in the internal conformational dynamics of Mcl1 with respect to binding affinity values of inhibitors. Further, the binding free energy estimation, using three different samples, was performed on the MD simulations and revealed that the predicted energies (ΔGmmgbsa) were in good correlation with the experimental values (ΔGexpt). Also, the energies obtained using all sampling models were efficiently ranked. Subsequently, the decomposition energy analysis highlighted the major energy-contributing residues at the Mcl1 binding pocket. Computational alanine scanning performed on high energy-contributing residues predicted the hot spot residues. The dihedral angle analysis using MD snapshots on the predicted hot spot residue exhibited consistency in side chain conformational motion that ultimately led to strong binding affinity values. The findings from the present study might provide valuable guidelines for the design of novel Mcl1 inhibitors that might significantly improve the specificity for new-generation chemotherapeutic agents.

Deciphering the crucial molecular properties of a series of Benzothiazole Hydrazone inhibitors that targets anti-apoptotic Bcl-xL protein.

The Bcl-2 family proteins are the central regulators of apoptosis. Due to its predominant role in cancer progression, the Bcl-2 family proteins act as attractive therapeutic targets. Recently, molecular series of Benzothiazole Hydrazone (BH) inhibitors that exhibits drug-likeness characteristics, which selectively targets Bcl-xL have been reported. In the present study, docking was used to explore the plausible binding mode of the highly active BH inhibitor with Bcl-xL; and Molecular Dynamics (MD) simulation was applied to investigate the stability of predicted conformation over time. Furthermore, the molecular properties of the series of BH inhibitors were extensively investigated by pharmacophore based 3D-QSAR model. The docking correctly predicted the binding mode of the inhibitor inside the Bcl-xL hydrophobic groove, whereas the MD-based free energy calculation exhibited the binding strength of the complex over the time period. Furthermore, the residue decomposition analysis revealed the major energy contributing residues - F105, L108, L130, N136, and R139 - involved in complex stability. Additionally, a six-featured pharmacophore model - AAADHR.89 - was developed using the series of BH inhibitors that exhibited high survival score. The statistically significant 3D-QSAR model exhibited high correlation co-efficient (R2 = .9666) and cross validation co-efficient (Q2 = .9015) values obtained from PLS regression analysis. The results obtained from the current investigation might provide valuable insights for rational drug design of Bcl-xL inhibitor synthesis.

Deciphering the crucial residues involved in heterodimerization of Bak peptide and anti-apoptotic proteins for apoptosis.

B-cell lymphoma 2 (Bcl-2) family proteins are the central regulators of apoptosis, functioning via mitochondrial outer membrane permeabilization. The family members are involved in several stages of apoptosis regulation. The overexpression of the anti-apoptotic proteins leads to several cancer pathological conditions. This overexpression is modulated or inhibited by heterodimerization of pro-apoptotic BH3 domain or BH3-only peptides to the hydrophobic groove present at the surface of anti-apoptotic proteins. Additionally, the heterodimerization displayed differences in binding affinity profile among the pro-apoptotic peptides binding to anti-apoptotic proteins. In light of discovering the novel peptide/drug molecules that contain the potential to inhibit specific anti-apoptotic protein, it is necessary to understand the molecular basis of recognition between the protein and its binding partner (peptide or ligand) along with its binding energies. Therefore, the present work focused on deciphering the molecular basis of recognition between pro-apoptotic Bak peptide binding to different anti-apoptotic (Bcl-xL, Bfl-1, Bcl-W, Mcl-1, and Bcl-2) proteins using advanced Molecular Dynamics (MD) approach such as Molecular Mechanics-Generalized Born Solvent Accessible. The results from our investigation revealed that the predicted binding free energies showed excellent correlation with the experimental values (r2 = .95). The electrostatic (ΔGele) contributions are the major component that drives the interaction between Bak peptides and different anti-apoptotic peptides. Additionally, van der Waals (ΔGvdw) energies also play an indispensible role in determining the binding free energy. Furthermore, the decomposition analysis highlighted the comprehensive information about the energy contributions of hotspot residues involved in stabilizing the interaction between Bak peptide and different anti-apoptotic proteins.

Probing the binding mechanism of mercaptoguanine derivatives as inhibitors of HPPK by docking and molecular dynamics simulations.

6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) is a promising antimicrobial target involved in the folate biosynthesis pathway. Although, the results from crystallographic studies of HPPK have attracted a great interest in the design of novel HPPK inhibitors, the mechanism of action of HPPK due to inhibitor binding remains questionable. Recently, mercaptoguanine derivatives were reported to inhibit the pyrophosphoryl transfer mechanism of Staphylococcus aureus HPPK (SaHPPK). The present study is an attempt to understand the SaHPPK-inhibitors binding mechanism and to highlight the key residues that possibly involve in the complex formation. To decipher these questions, we used the state-of-the-art advanced insilico approach such as molecular docking, molecular dynamics (MD), molecular mechanics-generalized Born surface area approach. Domain cross correlation and principle component analysis were applied to the snapshots obtained from MD revealed that the compounds with high binding affinity stabilize the conformational dynamics of SaHPPK. The binding free energy estimation showed that the van der Waals and electrostatic interactions played a vital role for the binding mechanism. Additionally, the predicted binding free energy was in good agreement with the experimental values (R2 = .78). Moreover, the free energy decomposition on per-residue confirms the key residues that significantly contribute to the complex formation. These results are expected to be useful for rational design of novel SaHPPK inhibitors.

Stage-specific binding profiles of cohesin in resting and activated B lymphocytes suggest a role for cohesin in immunoglobulin class switching and maturation.

The immunoglobulin heavy chain locus (Igh) features higher-order chromosomal interactions to facilitate stage-specific assembly of the Ig molecule. Cohesin, a ring-like protein complex required for sister chromatid cohesion, shapes chromosome architecture and chromatin interactions important for transcriptional regulation and often acts together with CTCF. Cohesin is likely involved in B cell activation and Ig class switch recombination. Hence, binding profiles of cohesin in resting mature murine splenic B lymphocytes and at two stages after cell activation were elucidated by chromatin immunoprecipitation and deep sequencing. Comparative genomic analysis revealed cohesin extensively changes its binding to transcriptional control elements after 48 h of stimulation with LPS/IL-4. Cohesin was clearly underrepresented at switch regions regardless of their activation status, suggesting that switch regions need to be cohesin-poor. Specific binding changes of cohesin at B-cell specific gene loci Pax5 and Blimp-1 indicate new cohesin-dependent regulatory pathways. Together with conserved cohesin/CTCF sites at the Igh 3'RR, a prominent cohesin/CTCF binding site was revealed near the 3' end of Cα where PolII localizes to 3' enhancers. Our study shows that cohesin likely regulates B cell activation and maturation, including Ig class switching.

Assessing computational methods for transcription factor target gene identification based on ChIP-seq data.

Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) has great potential for elucidating transcriptional networks, by measuring genome-wide binding of transcription factors (TFs) at high resolution. Despite the precision of these experiments, identification of genes directly regulated by a TF (target genes) is not trivial. Numerous target gene scoring methods have been used in the past. However, their suitability for the task and their performance remain unclear, because a thorough comparative assessment of these methods is still lacking. Here we present a systematic evaluation of computational methods for defining TF targets based on ChIP-seq data. We validated predictions based on 68 ChIP-seq studies using a wide range of genomic expression data and functional information. We demonstrate that peak-to-gene assignment is the most crucial step for correct target gene prediction and propose a parameter-free method performing most consistently across the evaluation tests.