Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

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.

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.

G-protein coupled receptors (GPCRs) are one of the largest groups of membrane proteins and are popular drug targets. The work reported here attempts to perform cross-genome phylogeny on GPCRs from two widely different taxa, human versus C. elegans genomes and to address the issues on evolutionary plasticity, to identify functionally related genes, orthologous relationship, and ligand binding properties through effective bioinformatic approaches. Through RPS blast around 1106 nematode GPCRs were given chance to associate with previously established 8 types of human GPCR profiles at varying E-value thresholds and resulted 32 clusters were illustrating co-clustering and class-specific retainsionship. In the significant thresholds, 81% of the C. elegans GPCRs were associated with 32 clusters and 27 C. elegans GPCRs (2%) inferred for orthology. 177 hypothetical proteins were observed in cluster association and could be reliably associated with one of 32 clusters. Several nematode-specific GPCR clades were observed suggesting lineage-specific functional recruitment in response to environment.

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 (r = .95). The electrostatic (ΔG) contributions are the major component that drives the interaction between Bak peptides and different anti-apoptotic peptides. Additionally, van der Waals (ΔG) 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.

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.

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.

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 (R = .9666) and cross validation co-efficient (Q = .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.