Development of CDK-targeted scoring functions for prediction of binding affinity

Assessment of polystyrene nano plastics effect on human salivary α-amylase structural alteration: Insights from an in vitro and in silico study

The study found that the enzyme activity of human salivary α-amylase (α-AHS) was competitively inhibited by nanoplastic polystyrene (PS-NPs), with a half-inhibitory concentration (IC50) of 92 μg/mL, while the maximum reaction rate (Vmax) remained unchanged at 909 μg/mL•min. An increase in the concentration of PS-NPs led to a quenching of α-AHS fluorescence with a slight red shift, indicating a static mechanism. The binding constant (Ka) and quenching constant (Kq) were calculated to be 2.92 × 1011 M-1 and 1.078 × 1019 M-1• S-1 respectively, with a hill coefficient (n) close to one and an apparent binding equilibrium constant (KA) of 1.54 × 1011 M-1. Molecular docking results suggested that the interaction between α-AHS and PS-NPs involved π-anion interactions between the active site Asp197, Asp300 residues, and van der Waals force interactions affecting the Tyr, Trp, and other residues. Fourier transform infrared (FT-IR) and circular dichroism (CD) analyses revealed conformational changes in α-AHS, including a loss of secondary structure α-helix and β-sheet. The study concludes that the interaction between α-AHS and PS-NPs leads to structural and functional changes in α-AHS, potentially impacting human health. This research provides a foundation for further toxicological analysis of MPs/NPs in the human digestive system.

TSSF-hERG: A machine-learning-based hERG potassium channel-specific scoring function for chemical cardiotoxicity prediction

The human ether-à-go-go-related gene (hERG) encodes the Kv11.1 voltage-gated potassium ion (K⁺) channel that conducts the rapidly activating delayed rectifier current (I_Kr) in cardiomyocytes to regulate the repolarization process. Some drugs, as blockers of hERG potassium channels, cannot be marketed due to prolonged QT intervals, as well known as cardiotoxicity. Predetermining the binding affinity values between drugs and hERG through in silico methods can greatly reduce the time and cost required for experimental verification. In this study, we collected 9,215 compounds with AutoDock Vina's docking structures as training set, and collected compounds from four references as test sets. A series of models for predicting the binding affinities of hERG blockers were built based on five machine learning algorithms and combinations of interaction features and ligand features. The model built by support vector regression (SVR) using the combination of all features achieved the best performance on both tenfold cross-validation and external verification, which was selected and named as TSSF-hERG (target-specific scoring function for hERG). TSSF-hERG is more accurate than the classic scoring function of AutoDock Vina and the machine-learning-based generic scoring function RF-Score, with a Pearson's correlation coefficient (Rp) of 0.765, a Spearman's rank correlation coefficient (Rs) of 0.757, a root-mean-square error (RMSE) of 0.585 in a tenfold cross-validation study. All results demonstrated that TSSF-hERG would be useful for improving the power of binding affinity prediction between hERG and compounds, which can be further used for prediction or virtual screening of the hERG-related cardiotoxicity of drug candidates.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

BACKGROUND

Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs.

OBJECTIVE

Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes.

METHODS

SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions.

RESULTS

Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions.

CONCLUSION

Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.

Alcoholism and Osteoimmunology

BACKGROUND

Chronic consumption of alcohol has an adverse effect on the skeletal system, which may lead to osteoporosis, delayed fracture healing and osteonecrosis of the femoral head. Currently, the treatment is limited, therefore, there is an urgent need to determine the underline mechanism and develop a new treatment. It is well-known that normal bone remodeling relies on the balance between osteoclast-mediated bone resorption and - mediated bone formation. Various factors can destroy the balance, including the dysfunction of the immune system. In this review, we summarized the relevant research in the alcoholic osteopenia with a focus on the abnormal osteoimmunology signals. We provided a new theoretical basis for the prevention and treatment of the alcoholic bone.

METHODS

We searched PubMed for publications from 1 January 1980 to 1 February 2020 to identify relevant and recent literature, summarizing evaluation and the prospect of alcoholic osteopenia. Detailed search terms were 'alcohol', 'alcoholic osteoporosis', 'alcoholic osteopenia' 'immune', 'osteoimmunology', 'bone remodeling', 'osteoporosis treatment' and 'osteoporosis therapy'.

RESULTS

A total of 135 papers are included in the review. About 60 papers described the mechanisms of alcohol involved in bone remodeling. Some papers were focused on the pathogenesis of alcohol on bone through osteoimmune mechanisms.

CONCLUSION

There is a complex network of signals between alcohol and bone remodeling and intercellular communication of osteoimmune may be a potential mechanism for alcoholic bone. Studying the osteoimmune mechanism is critical for drug development specific to the alcoholic bone disorder.

Targeting Mammalian Target of Rapamycin: Prospects for the Treatment of Inflammatory Bowel Diseases

Inflammatory bowel disease (IBD) is a general term for a group of chronic and progressive disorders. Several cellular and biomolecular pathways are implicated in the pathogenesis of IBD, yet the etiology is unclear. Activation of the mammalian target of rapamycin (mTOR) pathway in the intestinal epithelial cells was also shown to induce inflammation. This review focuses on the inhibition of the mTOR signaling pathway and its potential application in treating IBD. We also provide an overview of plant-derived compounds that are beneficial for the IBD management through modulation of the mTOR pathway. Data were extracted from clinical, in vitro and in vivo studies published in English between 1995 and May 2019, which were collected from PubMed, Google Scholar, Scopus and Cochrane library databases. Results of various studies implied that inhibition of the mTOR signaling pathway downregulates the inflammatory processes and cytokines involved in IBD. In this context, a number of natural products might reverse the pathological features of the disease. Furthermore, mTOR provides a novel drug target for IBD. Comprehensive clinical studies are required to confirm the efficacy of mTOR inhibitors in treating IBD.

Impact of Epigenetic Alterations in the Development of Oral Diseases

BACKGROUND

Epigenetic mechanisms alter gene expression and regulate vital cellular processes that contribute to the onset and progression of major dental diseases. Their reversible character may prove beneficial for therapeutic targeting. This review aims to provide an update on the main epigenetic changes that contribute to the pathogenesis of Oral Squamous Cell Carcinoma (OSCC), pulpitis and periodontitis as well as dental caries and congenital orofacial malformations, in an effort to identify potential therapeutic targets.

METHODS

We undertook a structured search of bibliographic databases (PubMed and MEDLINE) for peer-reviewed epigenetic research studies focused on oral diseases in the last ten years. A qualitative content analysis was performed in screened papers and a critical discussion of main findings is provided.

RESULTS

Several epigenetic modifications have been associated with OSCC pathogenesis, including promoter methylation of genes involved in DNA repair, cell cycle regulation and proliferation leading to malignant transformation. Additionally, epigenetic inactivation of tumor suppressor genes, overexpression of histone chaperones and several microRNAs are implicated in OSCC aggressiveness. Changes in the methylation patterns of IFN-γ and trimethylation of histone Η3Κ27 have been detected in pulpitis, along with an aberrant expression of several microRNAs, mainly affecting cytokine production. Chronic periodontal disease has been associated with modifications in the methylation patterns of Toll-Like Receptor 2, Prostaglandin synthase 2, E-cadherin and some inflammatory cytokines, along with the overexpression of miR-146a and miR155. Furthermore, DNA methylation was found to regulate amelogenesis and has been implicated in the pathogenesis of dental caries as well as in several congenital orofacial malformations.

CONCLUSION

Strong evidence indicates that epigenetic changes participate in the pathogenesis of oral diseases and epigenetic targeting may be considered as a complementary therapeutic scheme to the current management of oral health.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

BACKGROUND

The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities.

OBJECTIVE

Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures.

METHODS

We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models.

RESULTS

Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data.

CONCLUSION

Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

NOB1: A Potential Biomarker or Target in Cancer

Human NIN1/RPN12 binding protein 1 homolog (NOB1), an RNA binding protein, is expressed ubiquitously in normal tissues such as the lung, liver, and spleen. Its core physiological function is to regulate protease activities and participate in maintaining RNA metabolism and stability. NOB1 is overexpressed in a variety of cancers, including pancreatic cancer, non-small cell lung cancer, ovarian cancer, prostate carcinoma, osteosarcoma, papillary thyroid carcinoma, colorectal cancer, and glioma. Although existing data indicate that NOB1 overexpression is associated with cancer growth, invasion, and poor prognosis, the molecular mechanisms behind these effects and its exact roles remain unclear. Several studies have confirmed that NOB1 is clinically relevant in different cancers, and further research at the molecular level will help evaluate the role of NOB1 in tumors. NOB1 has become an attractive target in anticancer therapy because it is overexpressed in many cancers and mediates different stages of tumor development. Elucidating the role of NOB1 in different signaling pathways as a potential cancer treatment will provide new ideas for existing cancer treatment methods. This review summarizes the research progress made into NOB1 in cancer in the past decade; this information provides valuable clues and theoretical guidance for future anticancer therapy by targeting NOB1.

Molecular Dynamics Simulations with NAMD2

X-ray diffraction crystallography is the primary technique to determine the three-dimensional structures of biomolecules. Although a robust method, X-ray crystallography is not able to access the dynamical behavior of macromolecules. To do so, we have to carry out molecular dynamics simulations taking as an initial system the three-dimensional structure obtained from experimental techniques or generated using homology modeling. In this chapter, we describe in detail a tutorial to carry out molecular dynamics simulations using the program NAMD2. We chose as a molecular system to simulate the structure of human cyclin-dependent kinase 2.

Docking with AutoDock4

AutoDock is one of the most popular receptor-ligand docking simulation programs. It was first released in the early 1990s and is in continuous development and adapted to specific protein targets. AutoDock has been applied to a wide range of biological systems. It has been used not only for protein-ligand docking simulation but also for the prediction of binding affinity with good correlation with experimental binding affinity for several protein systems. The latest version makes use of a semi-empirical force field to evaluate protein-ligand binding affinity and for selecting the lowest energy pose in docking simulation. AutoDock4.2.6 has an arsenal of four search algorithms to carry out docking simulation including simulated annealing, genetic algorithm, and Lamarckian algorithm. In this chapter, we describe a tutorial about how to perform docking with AutoDock4. We focus our simulations on the protein target cyclin-dependent kinase 2.

Consensus Docking in Drug Discovery

Background:

Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets.

Methods:

In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose.

Results:

The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus.

Conclusion:

In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Biopolymer Substrates in Buccal Drug Delivery: Current Status and Future Trend

BACKGROUND

This paper provides a critical review of biopolymer-based substrates, especially the cellulose derivatives, for their application in buccal drug delivery. Drug delivery to the buccal mucous has the benefits of immobile muscle, abundant vascularization and rapid recovery, but not all the drugs can be administered through the buccal mucosa (e.g., macromolecular drugs), due to the low bioavailability caused by their large molecular size. This shortfall inspired the rapid development of drug-compounding technologies and the corresponding usage of biopolymer substrates.

METHODS

Cellulose derivatives have been extensively developed for drug manufacturing to facilitate its delivery. We engaged in structured research of cellulose-based drug compounding technologies. We summarized the characteristic cellulose derivatives which have been used as the biocompatible substrates in buccal delivery systems. The discussion of potential use of the rapidly-developed nanocellulose (NC) is also notable in this paper.

RESULTS

Seventy-eight papers were referenced in this perspective paper with the majority (sixty-five) published later than 2010. Forty-seven papers defined the buccal drug delivery systems and their substrates. Fifteen papers outlined the properties and applications of cellulose derivatives. Nanocellulose was introduced as a leading edge of nanomaterial with sixteen papers highlighted its adaptability in drug compounding for buccal delivery.

CONCLUSION

The findings of this perspective paper proposed the potential use of cellulose derivatives, the typical kind of biopolymers, in the buccal drug delivery system for promoting the bioavailability of macromolecular drugs. Nanocellulose (NC) in particular was proposed as an innovative bio-binder/carrier for the controlled-release of drugs in buccal system.

Cordycepin in Anticancer Research: Molecular Mechanism of Therapeutic Effects

Background:

Cordycepin is a nucleotide analogue from Cordyceps mushrooms, which occupies a notable place in traditional medicine.

Objective:

In this review article, we have discussed the recent findings on the molecular aspects of cordycepin interactions with its recognized cellular targets, and possible mechanisms of its anticancer activity.

Methods:

We have explored databases like pubmed, google scholar, scopus and web of science for the update information on cordycepin and mechanisms of its anticancer activity, and reviewed in this study.

Results:

Cordycepin has been widely recognized for its therapeutic potential against many types of cancers by various mechanisms. More specifically, cordycepin can induce apoptosis, resist cell cycle and cause DNA damage in cancer cells, and thus kill or control cancer cell growth. Also cordycepin can induce autophagy and modulate immune system. Furthermore, cordycepin also inhibits tumor metastasis. Although many success stories of cordycepin in anticancer research in vitro and in animal model, and there is no successful clinical trial yet.

Conclusion:

Ongoing research studies have reported highly potential anticancer activities of cordycepin with numerous molecular mechanisms. The in vitro and in vivo success of cordycepin in anticancer research might influence the clinical trials of cordycepin, and this molecule might be used for development of future cancer drug.

Structural Basis for Inhibition of Enoyl-[Acyl Carrier Protein] Reductase (InhA) from Mycobacterium tuberculosis

Background::

The enzyme trans-enoyl-[acyl carrier protein] reductase (InhA) is a central protein for the development of antitubercular drugs. This enzyme is the target for the pro-drug isoniazid, which is catalyzed by the enzyme catalase-peroxidase (KatG) to become active.

Objective::

Our goal here is to review the studies on InhA, starting with general aspects and focusing on the recent structural studies, with emphasis on the crystallographic structures of complexes involving InhA and inhibitors.

Method::

We start with a literature review, and then we describe recent studies on InhA crystallographic structures. We use this structural information to depict protein-ligand interactions. We also analyze the structural basis for inhibition of InhA. Furthermore, we describe the application of computational methods to predict binding affinity based on the crystallographic position of the ligands.

Results::

Analysis of the structures in complex with inhibitors revealed the critical residues responsible for the specificity against InhA. Most of the intermolecular interactions involve the hydrophobic residues with two exceptions, the residues Ser 94 and Tyr 158. Examination of the interactions has shown that many of the key residues for inhibitor binding were found in mutations of the InhA gene in the isoniazid-resistant Mycobacterium tuberculosis. Computational prediction of the binding affinity for InhA has indicated a moderate uphill relationship with experimental values.

Conclusion::

Analysis of the structures involving InhA inhibitors shows that small modifications on these molecules could modulate their inhibition, which may be used to design novel antitubercular drugs specific for multidrug-resistant strains.

Exploring the computational methods for protein-ligand binding site prediction

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

The role of human in the loop: lessons from D3R challenge 4

The rapid development of new machine learning techniques led to significant progress in the area of computer-aided drug design. However, despite the enormous predictive power of new methods, they lack explainability and are often used as black boxes. The most important decisions in drug discovery are still made by human experts who rely on intuitions and simplified representation of the field. We used D3R Grand Challenge 4 to model contributions of human experts during the prediction of the structure of protein-ligand complexes, and prediction of binding affinities for series of ligands in the context of absence or abundance of experimental data. We demonstrated that human decisions have a series of biases: a tendency to focus on easily identifiable protein-ligand interactions such as hydrogen bonds, and neglect for a more distributed and complex electrostatic interactions and solvation effects. While these biases still allow human experts to compete with blind algorithms in some areas, the underutilization of the information leads to significantly worse performance in data-rich tasks such as binding affinity prediction.

How Docking Programs Work

Protein-ligand docking simulations are of central interest for computer-aided drug design. Docking is also of pivotal importance to understand the structural basis for protein-ligand binding affinity. In the last decades, we have seen an explosion in the number of three-dimensional structures of protein-ligand complexes available at the Protein Data Bank. These structures gave further support for the development and validation of in silico approaches to address the binding of small molecules to proteins. As a result, we have now dozens of open source programs and web servers to carry out molecular docking simulations. The development of the docking programs and the success of such simulations called the attention of a broad spectrum of researchers not necessarily familiar with computer simulations. In this scenario, it is essential for those involved in experimental studies of protein-ligand interactions and biophysical techniques to have a glimpse of the basics of the protein-ligand docking simulations. Applications of protein-ligand docking simulations to drug development and discovery were able to identify hits, inhibitors, and even drugs. In the present chapter, we cover the fundamental ideas behind protein-ligand docking programs for non-specialists, which may benefit from such knowledge when studying molecular recognition mechanism.

Integration of Bioinformatics and in vitro Analysis Reveal Anti-leishmanial Effects of Azithromycin and Nystatin

Background:

Leishmaniasis is the major cause of mortality in under-developed countries. One of the main problems in leishmaniasis is the limited number of drug options, resistance and side effects. Such a situation requires to study the new chemical series with anti-leishmanial activity.

Objective:

To assess the anti-leishmanial activity of antibacterial and antifungal drugs.

Methods:

We have applied an integrative approach based on computational and in vitro methods to elucidate the efficacy of different antibacterial and antifungal drugs against Leishmania tropica (KWH23). Firstly these compounds were analyzed using in silico molecular docking. This analysis showed that the nystatin and azithromycin interacted with the active site amino acids of the target protein leishmanolysin. The nystatin, followed by azithromycin, produced the lowest binding energies indicating their inhibitive activity against the target. The efficacy of the docked drugs was further validated in vitro which showed that our bioinformatics based predictions completely agreed with experimental results. Stock solutions of drugs, media preparation and parasites cultures were performed according to the standard in-vitro protocol.

Results:

We found that the half maximal inhibitory concentration (IC50) value of dosage form of nystatin (10,000,00 U) and pure nystatin was 0.05701 µM and 0.00324 µM respectively. The IC50 value of combined azithromycin and nystatin (dosage and pure form) was 0.156 µg/ml and 0.0023 µg /ml (0.00248 µM) respectively. It was observed that IC50 value of nystatin is better than azithromycin and pure form of drugs had significant activity than the dosage form of drugs.

Conclusion:

From these results, it was also proven that pure drugs combination result is much better than all tested drugs results. The results of both in vitro and in silico studies clearly indicated that comparatively, nystatin is the potential candidate drug in combat against Leishmania tropica.

Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference

Background:

Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference.

Objective:

To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution.

Method:

In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list.

Results:

Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source.

Conclusion:

In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.

Docking with GemDock

GEMDOCK is a protein-ligand docking software that makes use of an elegant biologically inspired computational methodology based on the differential evolution algorithm. As any docking program, GEMDOCK has two major features to predict the binding of a small-molecule ligand to the binding site of a protein target: the search algorithm and the scoring function to evaluate the generated poses. The GEMDOCK scoring function uses a piecewise potential energy function integrated into the differential evolutionary algorithm. GEMDOCK has been applied to a wide range of protein systems with docking accuracy similar to other docking programs such as Molegro Virtual Docker, AutoDock4, and AutoDock Vina. In this chapter, we explain how to carry out protein-ligand docking simulations with GEMDOCK. We focus this tutorial on the protein target cyclin-dependent kinase 2.

Electrostatic Energy in Protein-Ligand Complexes

Computational analysis of protein-ligand interactions is of pivotal importance for drug design. Assessment of ligand binding energy allows us to have a glimpse of the potential of a small organic molecule as a ligand to the binding site of a protein target. Considering scoring functions available in docking programs such as AutoDock4, AutoDock Vina, and Molegro Virtual Docker, we could say that they all rely on equations that sum each type of protein-ligand interactions to model the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. In this chapter, we present the main physics concepts necessary to understand electrostatics interactions relevant to molecular recognition of a ligand by the binding pocket of a protein target. Moreover, we analyze the electrostatic potential energy for an ensemble of structures to highlight the main features related to the importance of this interaction for binding affinity.

SAnDReS: A Computational Tool for Docking

Since the early 1980s, we have witnessed considerable progress in the development and application of docking programs to assess protein-ligand interactions. Most of these applications had as a goal the identification of potential new binders to protein targets. Another remarkable progress is taking place in the determination of the structures of protein-ligand complexes, mostly using X-ray diffraction crystallography. Considering these developments, we have a favorable scenario for the creation of a computational tool that integrates into one workflow all steps involved in molecular docking simulations. We had these goals in mind when we developed the program SAnDReS. This program allows the integration of all computational features related to modern docking studies into one workflow. SAnDReS not only carries out docking simulations but also evaluates several docking protocols allowing the selection of the best approach for a given protein system. SAnDReS is a free and open-source (GNU General Public License) computational environment for running docking simulations. Here, we describe the combination of SAnDReS and AutoDock4 for protein-ligand docking simulations. AutoDock4 is a free program that has been applied to over a thousand receptor-ligand docking simulations. The dataset described in this chapter is available for downloading at https://github.com/azevedolab/sandres.

Van der Waals Potential in Protein Complexes

Van der Waals forces are determinants of the formation of protein-ligand complexes. Physical models based on the Lennard-Jones potential can estimate van der Waals interactions with considerable accuracy and with a computational complexity that allows its application to molecular docking simulations and virtual screening of large databases of small organic molecules. Several empirical scoring functions used to evaluate protein-ligand interactions approximate van der Waals interactions with the Lennard-Jones potential. In this chapter, we present the main concepts necessary to understand van der Waals interactions relevant to molecular recognition of a ligand by the binding pocket of a protein target. We describe the Lennard-Jones potential and its application to calculate potential energy for an ensemble of structures to highlight the main features related to the importance of this interaction for binding affinity.

Molegro Virtual Docker for Docking

Molegro Virtual Docker is a protein-ligand docking simulation program that allows us to carry out docking simulations in a fully integrated computational package. MVD has been successfully applied to hundreds of different proteins, with docking performance similar to other docking programs such as AutoDock4 and AutoDock Vina. The program MVD has four search algorithms and four native scoring functions. Considering that we may have water molecules or not in the docking simulations, we have a total of 32 docking protocols. The integration of the programs SAnDReS ( https://github.com/azevedolab/sandres ) and MVD opens the possibility to carry out a detailed statistical analysis of docking results, which adds to the native capabilities of the program MVD. In this chapter, we describe a tutorial to carry out docking simulations with MVD and how to perform a statistical analysis of the docking results with the program SAnDReS. To illustrate the integration of both programs, we describe the redocking simulation focused the cyclin-dependent kinase 2 in complex with a competitive inhibitor.

Molecular Docking Simulations with ArgusLab

Molecular docking is the major computational technique employed in the early stages of computer-aided drug discovery. The availability of free software to carry out docking simulations of protein-ligand systems has allowed for an increasing number of studies using this technique. Among the available free docking programs, we discuss the use of ArgusLab ( http://www.arguslab.com/arguslab.com/ArgusLab.html ) for protein-ligand docking simulation. This easy-to-use computational tool makes use of a genetic algorithm as a search algorithm and a fast scoring function that allows users with minimal experience in the simulations of protein-ligand simulations to carry out docking simulations. In this chapter, we present a detailed tutorial to perform docking simulations using ArgusLab.

Homology Modeling of Protein Targets with MODELLER

Homology modeling is a computational approach to generate three-dimensional structures of protein targets when experimental data about similar proteins are available. Although experimental methods such as X-ray crystallography and nuclear magnetic resonance spectroscopy successfully solved the structures of nearly 150,000 macromolecules, there is still a gap in our structural knowledge. We can fulfill this gap with computational methodologies. Our goal in this chapter is to explain how to perform homology modeling of protein targets for drug development. We choose as a homology modeling tool the program MODELLER. To illustrate its use, we describe how to model the structure of human cyclin-dependent kinase 3 using MODELLER. We explain the modeling procedure of CDK3 apoenzyme and the structure of this enzyme in complex with roscovitine.

Exploring the Scoring Function Space

In the analysis of protein-ligand interactions, two abstractions have been widely employed to build a systematic approach to analyze these complexes: protein and chemical spaces. The pioneering idea of the protein space dates back to 1970, and the chemical space is newer, later 1990s. With the progress of computational methodologies to create machine-learning models to predict the ligand-binding affinity, clearly there is a need for novel approaches to the problem of protein-ligand interactions. New abstractions are required to guide the conceptual analysis of the molecular recognition problem. Using a systems approach, we proposed to address protein-ligand scoring functions using the modern idea of the scoring function space. In this chapter, we describe the fundamental concept behind the scoring function space and how it has been applied to develop the new generation of targeted-scoring functions.

Machine Learning to Predict Binding Affinity

Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic coordinates of protein-ligand complexes. These new computational tools made application of a broad spectrum of machine-learning techniques to study protein-ligand interactions possible. The essential aspect of these machine-learning approaches is to train a new computational model by using technologies such as supervised machine-learning techniques, convolutional neural network, and random forest to mention the most commonly applied methods. In this chapter, we focus on supervised machine-learning techniques and their applications in the development of protein-targeted scoring functions for the prediction of binding affinity. We discuss the development of the program SAnDReS and its application to the creation of machine-learning models to predict inhibition of cyclin-dependent kinase and HIV-1 protease. Moreover, we describe the scoring function space, and how to use it to explain the development of targeted scoring functions.

Docking with SwissDock

Protein-ligand docking simulation is central in drug design and development. Therefore, the development of web servers intended to docking simulations is of pivotal importance. SwissDock is a web server dedicated to carrying out protein-ligand docking simulation intuitively and elegantly. SwissDock is based on the protein-ligand docking program EADock DSS and has a simple and integrated interface. The SwissDock allows the user to upload structure files for a protein and a ligand, and returns the results by e-mail. To facilitate the upload of the protein and ligand files, we can prepare these input files using the program UCSF Chimera. In this chapter, we describe how to use UCSF Chimera and SwissDock to perform protein-ligand docking simulations. To illustrate the process, we describe the molecular docking of the competitive inhibitor roscovitine against the structure of human cyclin-dependent kinase 2.

Hydrogen Bonds in Protein-Ligand Complexes

Fast and reliable evaluation of the hydrogen bond potential energy has a significant impact in the drug design and development since it allows the assessment of large databases of organic molecules in virtual screening projects focused on a protein of interest. Semi-empirical force fields implemented in molecular docking programs make it possible the evaluation of protein-ligand binding affinity where the hydrogen bond potential is a common term used in the calculation. In this chapter, we describe the concepts behind the programs used to predict hydrogen bond potential energy employing semi-empirical force fields as the ones available in the programs AMBER, AutoDock4, TreeDock, and ReplicOpter. We described here the 12-10 potential and applied it to evaluate the binding affinity for an ensemble of crystallographic structures for which experimental data about binding affinity are available.