Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2

Predicting mechanical properties of silk from its amino acid sequences via machine learning

The silk fiber is increasingly being sought for its superior mechanical properties, biocompatibility, and eco-friendliness, making it promising as a base material for various applications. One of the characteristics of protein fibers, such as silk, is that their mechanical properties are significantly dependent on the amino acid sequence. Numerous studies have been conducted to determine the specific relationship between the amino acid sequence of silk and its mechanical properties. Still, the relationship between the amino acid sequence of silk and its mechanical properties is yet to be clarified. Other fields have adopted machine learning (ML) to establish a relationship between the inputs, such as the ratio of different input material compositions and the resulting mechanical properties. We have proposed a method to convert the amino acid sequence into numerical values for input and succeeded in predicting the mechanical properties of silk from its amino acid sequences. Our study sheds light on predicting mechanical properties of silk fiber from respective amino acid sequences.

Beyond sequence: Structure-based machine learning

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.

Natural Products Against COVID-19 Inflammation: A Mini-Review

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a virus whose genetic material is positive single-stranded RNA, being responsible for coronavirus disease 2019 (COVID- 19), an infection that compromises the lungs and consequently the respiratory capacity of the infected individual, according to the WHO in November 2021, 249,743,428 cases were confirmed, of which 5,047,652 individuals died due to complications resulting from the infection caused by SARSCOV- 2. As the infection progresses, the individual may experience loss of smell and taste, as well as breathing difficulties, severe respiratory failure, multiple organ failure, and death. Due to this new epidemiological agent in March 2020 it was announced by the director general of the World Health Organization (WHO) a pandemic status, and with that, many research groups are looking for new therapeutic alternatives through synthetic and natural bioactives. This research is a literature review of some in silico studies involving natural products against COVID-19 inflammation published in 2020 and 2021. Work like this presents relevant information to the scientific community, boosting future research and encouraging the use of natural products for the search for new antivirals against COVID-19.

Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.

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.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

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.

In depth analysis on the carbohydrate-binding properties of a vasorelaxant lectin from Dioclea lasiophylla Mart Ex. Benth seeds

Lectins are a class of proteins or glycoproteins capable of recognizing and interacting with carbohydrates in a specific and reversible manner. Owing to this property, these proteins can interact with glycoconjugates present on the cell surface, making it possible to decipher the glycocode, as well as elicit biological effects, such as inflammation and vasorelaxation. Here, we report a structural and biological study of the mannose/glucose-specific lectin from Dioclea lasiophylla seeds, DlyL. The study aimed to evaluate in detail the interaction of DlyL with Xman and high-mannose N-glycans (MAN3, MAN5 and MAN9) by molecular dynamics (MD) and the resultant in vitro effect on vasorelaxation using rat aortic rings. In silico analysis of molecular docking was performed to obtain the initial coordinates of the DlyL complexes with the carbohydrates to apply as inputs in MD simulations. The MD trajectories demonstrated the stability of DlyL over time as well as different profiles of interaction with Xman and N-glycans. Furthermore, aortic rings assays demonstrated that the lectin could relax pre-contracted aortic rings with the participation of the carbohydrate recognition domain (CRD) and nitric oxide (NO) when endothelial tissue is preserved. These results confirm the ability of DlyL to interact with high-mannose N-glycans with its expanded CRD, supporting the hypothesis that DlyL vasorelaxant activity occurs primarily through its interaction with cell surface glycosylated receptors.Communicated by Ramaswamy H. Sarma.

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.

ConBr lectin modulates MAPKs and Akt pathways and triggers autophagic glioma cell death by a mechanism dependent upon caspase-8 activation

Glioblastoma multiforme is the most aggressive type of glioma, with limited treatment and poor prognosis. Despite some advances over the last decade, validation of novel and selective antiglioma agents remains a challenge in clinical pharmacology. Prior studies have shown that leguminous lectins may exert various biological effects, including antitumor properties. Accordingly, this study aimed to evaluate the mechanisms underlying the antiglioma activity of ConBr, a lectin extracted from the Canavalia brasiliensis seeds. ConBr at lower concentrations inhibited C6 glioma cell migration while higher levels promoted cell death dependent upon carbohydrate recognition domain (CRD) structure. ConBr increased p38^MAPK and JNK and decreased ERK1/2 and Akt phosphorylation. Moreover, ConBr inhibited mTORC1 phosphorylation associated with accumulation of autophagic markers, such as acidic vacuoles and LC3 cleavage. Inhibition of early steps of autophagy with 3-methyl-adenine (3-MA) partially protected whereas the later autophagy inhibitor Chloroquine (CQ) had no protective effect upon ConBr cytotoxicity. ConBr also augmented caspase-3 activation without affecting mitochondrial function. Noteworthy, the caspase-8 inhibitor IETF-fmk attenuated ConBr induced autophagy and C6 glioma cell death. Finally, ConBr did not show cytotoxicity against primary astrocytes, suggesting a selective antiglioma activity. In summary, our results indicate that ConBr requires functional CRD lectin domain to exert antiglioma activity, and its cytotoxicity is associated with MAPKs and Akt pathways modulation and autophagy- and caspase-8- dependent cell death.

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.

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.

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.

In Silico Studies for Bacterystic Evaluation against Staphylococcus aureus of 2-Naphthoic Acid Analogues

Background:

Staphylococcus aureus is a gram-positive spherical bacterium commonly present in nasal fossae and in the skin of healthy people; however, in high quantities, it can lead to complications that compromise health. The pathologies involved include simple infections, such as folliculitis, acne, and delay in the process of wound healing, as well as serious infections in the CNS, meninges, lung, heart, and other areas.

Aim:

This research aims to propose a series of molecules derived from 2-naphthoic acid as a bioactive in the fight against S. aureus bacteria through in silico studies using molecular modeling tools.

Methods:

A virtual screening of analogues was done in consideration of the results that showed activity according to the prediction model performed in the KNIME Analytics Platform 3.6, violations of the Lipinski rule, absorption rate, cytotoxicity risks, energy of binder-receptor interaction through molecular docking, and the stability of the best profile ligands in the active site of the proteins used (PDB ID 4DXD and 4WVG).

Results:

Seven of the 48 analogues analyzed showed promising results for bactericidal action against S. aureus.

Conclusion:

It is possible to conclude that ten of the 48 compounds derived from 2-naphthoic acid presented activity based on the prediction model generated, of which seven presented no toxicity and up to one violation to the Lipinski rule.

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.

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.

Advances in the Understanding of the Cannabinoid Receptor 1 – Focusing on the Inverse Agonists Interactions

Background:

Cannabinoid Receptor 1 (CB1) is a membrane protein prevalent in the central nervous system, whose crystallographic structure has recently been solved. Studies will be needed to investigate CB1 complexes with its ligands and its role in the development of new drugs.

Objective:

Our goal here is to review the studies on CB1, starting with general aspects and focusing on the recent structural studies, with emphasis on the inverse agonists bound structures.

Methods:

We start with a literature review, and then we describe recent studies on CB 1 crystallographic structure and docking simulations. We use this structural information to depict protein-ligand interactions. We also describe the molecular docking method to obtain complex structures of CB 1 with inverse agonists.

Results:

Analysis of the crystallographic structure and docking results revealed the residues responsible for the specificity of the inverse agonists for CB 1. Most of the intermolecular interactions involve hydrophobic residues, with the participation of the residues Phe 170 and Leu 359 in all complex structures investigated in the present study. For the complexes with otenabant and taranabant, we observed intermolecular hydrogen bonds involving residues His 178 (otenabant) and Thr 197 and Ser 383 (taranabant).

Conclusion:

Analysis of the structures involving inverse agonists and CB 1 revealed the pivotal role played by residues Phe 170 and Leu 359 in their interactions and the strong intermolecular hydrogen bonds highlighting the importance of the exploration of intermolecular interactions in the development of novel inverse agonists.

Progress of Individualized Chemotherapy for Gastric Carcinoma Under the Guidance of Genetic Testing

BACKGROUND

Gastric cancer is a major malignancy that has high incidence rates worldwide. Approximately 30% of patients with gastric cancer have progressed into advanced stages at the time of diagnosis. Chemotherapy is the standard-of-care for most advanced gastric cancer and elicits variable responses among patients. Personalized chemotherapy based on genetic information of individual patients with gastric cancer has gained increasing attention among oncologists for guiding chemotherapeutic regimens.

METHODS

This review summarizes recent progress of individualized chemotherapy in gastric cancer guided by pharmacogenomics. Variable medical research search engines, such as PubMed, Google Scholar, SpringerLink and ScienceDirect, were used to retrieve related literature. Only peerreviewed journal articles were selected for further analyses.

RESULTS AND CONCLUSION

The efficiency of chemotherapy in patients with gastric cancer is not only determined by chemotherapeutic drugs but is also directly and indirectly influenced by functionally correlative genes. Individual gene alteration or polymorphism remarkably affects patients' responses to particular chemotherapy. Most studies have focused on the influence of single-gene alteration on a selected drug, and only a few works explored the interaction between therapeutics and a panel of genes. Individualized chemotherapy regimens guided by a genetic survey of a multiple-gene panel are expected to remarkably improve the treatment efficacy in patients with advanced gastric cancer and may become the new standard for personalizing chemotherapy for gastric cancer in the near future.

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.

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.

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.

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.

Hispaglabridin B, a constituent of liquorice identified by a bioinformatics and machine learning approach, relieves protein-energy wasting by inhibiting forkhead box O1

BACKGROUND AND PURPOSE

Liquorice is the root of Glycyrrhiza glabra, which is a popular food in Europe and China that has previously shown benefits for skeletal fatigue and nutrient metabolism. However, the mechanism and active ingredients remain largely unclear. The aim of this study was to investigate the active ingredients of liquorice for muscle wasting and elucidate the underlying mechanisms.

EXPERIMENTAL APPROACH

RNA-Seq and bioinformatics analysis were applied to predict the main target of liquorice. A machine learning model and a docking tool were used to predict active ingredients. Isotope labelling experiments, immunostaining, Western blots, qRT-PCR, ChIP-PCR and luciferase reporters were utilized to test the pharmacological effects in vitro and in vivo. The reverse effects were verified through recombination-based overexpression.

KEY RESULTS

The liposoluble constituents of liquorice improved muscle wasting by inhibiting protein catabolism and fibre atrophy. We further identified FoxO1 as the target of liposoluble constituents of liquorice. In addition, hispaglabridin B (HB) was predicted as an inhibitor of FoxO1. Further studies determined that HB improved muscle wasting by inhibiting catabolism in vivo and in vitro. HB also markedly suppressed the transcriptional activity of FoxO1, with decreased expression of the muscle-specific E3 ubiquitin ligases MuRF1 and Atrogin-1.

CONCLUSIONS AND IMPLICATIONS

HB can serve as a novel natural food extract for preventing muscle wasting in chronic kidney disease and possibly other catabolic conditions.

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.