ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research

Modulating autophagy to treat diseases: A revisited review on in silico methods

BACKGROUND

Autophagy refers to the conserved cellular catabolic process relevant to lysosome activity and plays a vital role in maintaining the dynamic equilibrium of intracellular matter by degrading harmful and abnormally accumulated cellular components. Accumulating evidence has recently revealed that dysregulation of autophagy by genetic and exogenous interventions may disrupt cellular homeostasis in human diseases. In silico approaches as powerful aids to experiments have also been extensively reported to play their critical roles in the storage, prediction, and analysis of massive amounts of experimental data. Thus, modulating autophagy to treat diseases by in silico methods would be anticipated.

AIM OF REVIEW

Here, we focus on summarizing the updated in silico approaches including databases, systems biology network approaches, omics-based analyses, mathematical models, and artificial intelligence (AI) methods that sought to modulate autophagy for potential therapeutic purposes, which will provide a new insight into more promising therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Autophagy-related databases are the data basis of the in silico method, storing a large amount of information about DNA, RNA, proteins, small molecules and diseases. The systems biology approach is a method to systematically study the interrelationships among biological processes including autophagy from a macroscopic perspective. Omics-based analyses are based on high-throughput data to analyze gene expression at different levels of biological processes involving autophagy. mathematical models are visualization methods to describe the dynamic process of autophagy, and its accuracy is related to the selection of parameters. AI methods use big data related to autophagy to predict autophagy targets, design targeted small molecules, and classify diverse human diseases for potential therapeutic applications.

Insights from the molecular mechanism of pyrazinamide to mutated pyrazinamidase linked to the pncA gene in clinical isolates of Mycobacterium tuberculosis

This study aims to conduct a comprehensive molecular dynamics strategy to evaluate whether mutations found in pyrazinamide monoresistant (PZAMR) strains of Mycobacterium tuberculosis (MTB) can potentially reduce the effectiveness of pyrazinamide (PZA) for tuberculosis (TB) treatment. Five single point mutations of pyrazinamidase (PZAse), an enzyme which is responsible for the activation of prodrug PZA into pyrazinoic acid, found in MTB clinical isolates, namely His82Arg, Thr87Met, Ser66Pro, Ala171Val, and Pro62Leu, were analyzed by the dynamics simulations both in the apo state (unbound state) and in the PZA bound state. The results showed that the mutation of His82 to Arg, Thr87 to Met, and Ser66 to Pro in PZAse affects the coordination state of the Fe2+ ion, which is a cofactor required for enzyme activity. These mutations change the flexibility, stability, and fluctuation of His51, His57, and ASP49 amino acid residues around the Fe2+ ion, culminating in an unstable complex and dissociation of PZA from the PZAse binding site. However, mutations of Ala171 to Val and Pro62 to Leu were found to have no effect on the complex's stability. Based on the results, PZAse mutations of His82Arg, Thr87Met, and Ser66Pro culminated in weak binding affinity for PZA and caused significant structural deformations that led to PZA resistance. Future structural and functional studies, as well as investigations into other aspects of drug resistance in PZAse, will require experimental clarification.Communicated by Ramaswamy H. Sarma.

Bioisosteric replacement based on 1,2,4-oxadiazoles in the discovery of 1H-indazole-bearing neuroprotective MAO B inhibitors

Following a hybridization strategy, a series of 5-substituted-1H-indazoles were designed and evaluated in vitro as inhibitors of human monoamine oxidase (hMAO) A and B. Among structural modifications, the bioisostere-based introduction of 1,2,4-oxadiazole ring returned the most potent and selective human MAO B inhibitor (compound 20, IC₅₀ = 52 nM, SI > 192). The most promising inhibitors were studied in cell-based neuroprotection models of SH-SY5Y and astrocytes line against H₂O₂. Moreover, preliminary drug-like features (aqueous solubility at pH 7.4; hydrolytic stability at acidic and neutral pH) were assessed for selected 1,2,4-oxadiazoles and compared to amide analogues through RP-HPLC methods. Molecular docking simulations highlighted the crucial role of molecular flexibility in providing a better shape complementarity for compound 20 within MAO B enzymatic cleft than rigid analogue 18. Enzymatic kinetics analysis along with thermal stability curves (T_m shift = +2.9 °C) provided clues of a tight-binding mechanism for hMAO B inhibition by 20.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance

Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all-against-all ensemble docking). Recent studies have shown that the performance of ensemble docking can be improved by selecting the most relevant protein structures for docking. In search for a robust approach to protein structure selection, we have come up with an integrated mAchine Learning AnD DockINg approach (ALADDIN). ALADDIN employs a battery of random forest classifiers to select, individually for each compound of interest, from an ensemble of protein structures, the single most suitable protein structure for docking. ALADDIN outperformed the best single-structure docking runs, ensemble docking and a similarity-based docking approach on three out of four investigated targets, with up to 0.15, 0.11 and 0.16 higher area under the receiver operating characteristic curve (AUC) values, respectively. Only in the case of cytochrome P450 3A4, ALADDIN, like any of the other tested approaches, failed to obtain decent performance. ALADDIN can be particularly useful for structure-based virtual screening of malleable proteins, including kinases, some viral enzymes and anti-targets.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking

Ensemble docking is a widely applied concept in structure-based virtual screening-to at least partly account for protein flexibility-usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases- and in this study as well-this is the minimum score). Nonetheless, there are a number of other fusion rules that can be applied. We report here the results of a detailed statistical comparison of seven fusion rules for ensemble docking, on five case studies of current drug targets, based on four performance metrics. Sevenfold cross-validation and variance analysis (ANOVA) allowed us to highlight the best fusion rules. The results are presented in bubble plots, to unite the four performance metrics into a single, comprehensive image. Notably, we suggest the use of the geometric and harmonic means as better alternatives to the generally applied minimum fusion rule.

Deciphering the Rules of in Silico Autophagy Methods for Expediting Medicinal Research

Autophagy is a highly evolutionarily conserved cellular catabolic process responsible for degradation of damaged organelles and long-lived proteins. It is not surprising that scientific interest in the connection of autophagy and diseases has been growing. Over the past 2 decades, many new experimental approaches have been emerging in the field of targeting autophagy for medicinal research. Interestingly, in addition to experimental methods, a number of databases, Web servers, mathematics models, omics approaches, and systems biology network approaches related to autophagy have become available online, which may be collectively considered to be "in silico autophagy methods" for expediting current medicinal research. Thus, we have summarized a series of relevant in silico autophagy approaches for promoting the most appropriate usage of these resources for potential therapeutic purposes.

Autophagy and Apoptosis Specific Knowledgebases-guided Systems Pharmacology Drug Research

BACKGROUND

Autophagy and apoptosis are the basic physiological processes in cells that clean up aged and mutant cellular components or even the entire cells. Both autophagy and apoptosis are disrupted in most major diseases such as cancer and neurological disorders. Recently, increasing attention has been paid to understand the crosstalk between autophagy and apoptosis due to their tightly synergetic or opposite functions in several pathological processes.

OBJECTIVE

This study aims to assist autophagy and apoptosis-related drug research, clarify the intense and complicated connections between two processes, and provide a guide for novel drug development.

METHODS

We established two chemical-genomic databases which are specifically designed for autophagy and apoptosis, including autophagy- and apoptosis-related proteins, pathways and compounds. We then performed network analysis on the apoptosis- and autophagy-related proteins and investigated the full protein-protein interaction (PPI) network of these two closely connected processes for the first time.

RESULTS

The overlapping targets we discovered show a more intense connection with each other than other targets in the full network, indicating a better efficacy potential for drug modulation. We also found that Death-associated protein kinase 1 (DAPK1) is a critical point linking autophagy- and apoptosis-related pathways beyond the overlapping part, and this finding may reveal some delicate signaling mechanism of the process. Finally, we demonstrated how to utilize our integrated computational chemogenomics tools on in silico target identification for small molecules capable of modulating autophagy- and apoptosis-related pathways.

CONCLUSION

The knowledge-bases for apoptosis and autophagy and the integrated tools will accelerate our work in autophagy and apoptosis-related research and can be useful sources for information searching, target prediction, and new chemical discovery.

Selection of protein conformations for structure-based polypharmacology studies

Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design.