Protein-Protein Interaction Modulators for Epigenetic Therapies

Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths globally, with limited diagnostic and clinically significant therapeutic targets. Identifying the genes and processes involved in developing and progressing LUAD is crucial for developing effective targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression profile datasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO) databases. The differential gene expression and the downstream analysis determined clinically significant biomarkers using a network-based approach. These therapeutic targets predominantly enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD patients. The hydrophobic residues of the AURKA-TPX2 interaction were considered as the target site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the AURKA destabilization. This in silico study lays the foundation for repurposing targeted therapeutic options to impede the Protein-Protein Interactions (PPIs) in LUAD progression and aid in future translational investigations.

Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials

Undruggable proteins are a class of proteins that are often characterized by large, complex structures or functions that are difficult to interfere with using conventional drug design strategies. Targeting such undruggable targets has been considered also a great opportunity for treatment of human diseases and has attracted substantial efforts in the field of medicine. Therefore, in this review, we focus on the recent development of drug discovery targeting "undruggable" proteins and their application in clinic. To make this review well organized, we discuss the design strategies targeting the undruggable proteins, including covalent regulation, allosteric inhibition, protein-protein/DNA interaction inhibition, targeted proteins regulation, nucleic acid-based approach, immunotherapy and others.

Machine Learning Models to Predict Protein-Protein Interaction Inhibitors

Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.

Targeting Undruggable Transcription Factors with PROTACs: Advances and Perspectives

Dysregulation of transcription factors has been implicated in a variety of human diseases. However, these proteins have traditionally been regarded as undruggable and only a handful of them have been successfully targeted by conventional small molecules. Moreover, the development of intrinsic and acquired resistance has hampered the clinical use of these agents. Over the past years, proteolysis-targeting chimeras (PROTACs) have shown great promise because of their potential for overcoming drug resistance and their ability to target previously undruggable proteins. Indeed, several small molecule-based PROTACs have demonstrated superior efficacy in therapy-resistant metastatic cancers. Nevertheless, it remains challenging to identify ligands for the majority of transcription factors. Given that transcription factors recognize short DNA motifs in a sequence-specific manner, multiple novel approaches exploit DNA motifs as warheads in PROTAC design for the degradation of aberrant transcription factors. These PROTACs pave the way for targeting undruggable transcription factors with potential therapeutic benefits.

Novel protein contact points among TP53 and minichromosome maintenance complex proteins 2, 3, and 5

OBJECTIVE

Identify protein contact points between TP53 and minichromosome maintenance (MCM) complex proteins 2, 3, and 5 with high resolution allowing for potential novel Cancer drug design.

METHODS

A next-generation sequencing-based protein-protein interaction method developed in our laboratory called AVA-Seq was applied to a gold-standard human protein interaction set. Proteins including TP53, MCM2, MCM3, MCM5, HSP90AA1, PCNA, NOD1, and others were sheared and ligated into the AVA-Seq system. Protein-protein interactions were then identified in both mild and stringent selective conditions.

RESULTS

Known interactions among MCM2, MCM3, and MCM5 were identified with the AVA-Seq system. The interacting regions detected between these three proteins overlap with the structural data of the MCM complex, and novel domains were identified with high resolution determined by multiple overlapping fragments. Fragments of wild type TP53 were shown to interact with MCM2, MCM3, and MCM5, and details on the location of the interactions were provided. Finally, a mini-network of known and novel cancer protein interactions was provided, which could have implications for fundamental changes in multiple cancers.

CONCLUSION

We provide a high-resolution mini-interactome that could direct novel drug targets and implicate possible effects of specific cancer mutations.

Epigenetic Modulators as Treatment Alternative to Diverse Types of Cancer

DNA is packaged in rolls in an octamer of histones forming a complex of DNA and proteins called chromatin. Chromatin as a structural matrix of a chromosome and its modifications are nowadays considered relevant aspects for regulating gene expression, which has become of high interest in understanding genetic mechanisms regulating various diseases, including cancer. In various types of cancer, the main modifications are found to be DNA methylation in the CpG dinucleotide as a silencing mechanism in transcription, post-translational histone modifications such as acetylation, methylation and others that affect the chromatin structure, the ATP-dependent chromatin remodeling and miRNA-mediated gene silencing. In this review we analyze the main alterations in gene expression, the epigenetic modification patterns that cancer cells present, as well as the main modulators and inhibitors of each epigenetic mechanism and the molecular evolution of the most representative inhibitors, which have opened a promising future in the study of HAT, HDAC, non-glycoside DNMT inhibitors and domain inhibitors.

Protein-Peptide Binding Site Detection Using 3D Convolutional Neural Networks

Peptides and peptide-based molecules represent a promising therapeutic modality targeting intracellular protein-protein interactions, potentially combining the beneficial properties of biologics and small-molecule drugs. Protein-peptide complexes occupy a unique niche of interaction interfaces with respect to protein-protein and protein-small molecule complexes. Protein-peptide binding site identification resembles image object detection, a field that had been revolutionalized with computer vision techniques. We present a new protein-peptide binding site detection method called BiteNet_Pp by harnessing the power of 3D convolutional neural network. Our method employs a tensor-based representation of spatial protein structures, which is fed to 3D convolutional neural network, resulting in probability scores and coordinates of the binding "hot spots" in the input structures. We used the domain adaptation technique to fine-tune model trained on protein-small molecule complexes using a manually curated set of protein-peptide structures. BiteNet_Pp consistently outperforms existing state-of-the-art methods in the independent test benchmark. It takes less than a second to analyze a single-protein structure, making BiteNet_Pp suitable for the large-scale analysis of protein-peptide binding sites.

Alteraciones Hepaticas en pacientes con infección por VIH en un centro de investigacion en Bogotá Colombia 2009 – 2019

Introducción: Las alteraciones de la bioquímica hepática son frecuentes en los pacientes con infección por VIH, la etiología es variada, la esteatosis hepática es frecuente con una prevalencia estimada del 60% Objetivos: Caracterizar las alteraciones hepáticas en una serie de pacientes con infección por VIH en un centro de investigación de Bogotá Colombia durante el periodo 2009 – 2019. Materiales y Métodos: Estudio descriptivo, retrospectivo, observacional de pacientes con infección por VIH que asistieron a un centro de investigación durante los años 2009-2019. Resultados: 94% fueron hombres y 6% mujeres con edad promedio de 44 años, 92,5% de los pacientes presentaba uso de terapia antiretroviral. Las principales hepatopatías fueron la coinfección VIH-Hepatitis C y el hígado graso en iguales porcentajes, 31,3%. El promedio del indice HOMA fue de 2,58. Discusión: Las enfermedades hepáticas son una causa importante de morbimortalidad en pacientes con infección por VIH, las coinfecciones virales y el hígado graso pueden ser muy frecuentes en nuestro medio a diferencia de otros estudios Conclusiones: Este es el primer estudio a nivel local en describir las alteraciones hepáticas en pacientes con VIH, las comorbilidades no SIDA, juegan un papel importante dentro de la enfermedad. La hepatitis C continúa siendo una coinfección frecuente en la población VIH.

A novel class of selective non-nucleoside inhibitors of human DNA methyltransferase 3A

Screening of a small chemical library (Medicines for Malaria Venture Pathogen Box) identified two structurally related pyrazolone (inhibitor 1) and pyridazine (inhibitor 2) DNMT3A inhibitors with low micromolar inhibition constants. The uncompetitive and mixed type inhibition patterns with DNA and AdoMet suggest these molecules act through an allosteric mechanism, and thus are unlikely to bind to the enzyme's active site. Unlike the clinically used mechanism based DNMT inhibitors such as decitabine or azacitidine that act via the enzyme active site, the inhibitors described here could lead to the development of more selective drugs. Both inhibitors show promising selectivity for DNMT3A in comparison to DNMT1 and bacterial DNA cytosine methyltransferases. With further study, this could form the basis of preferential targeting of de novo DNA methylation over maintenance DNA methylation.

Developing drugs for the 'undruggable'

There are up to 650,000 'undruggable' protein-protein interactions (PPIs) in the human interactome that can be potentially considered as novel therapeutic targets. How does the 'undruggable' become 'druggable'?

Recent advances in the development of protein-protein interactions modulators: mechanisms and clinical trials

Protein-protein interactions (PPIs) have pivotal roles in life processes. The studies showed that aberrant PPIs are associated with various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Therefore, targeting PPIs is a direction in treating diseases and an essential strategy for the development of new drugs. In the past few decades, the modulation of PPIs has been recognized as one of the most challenging drug discovery tasks. In recent years, some PPIs modulators have entered clinical studies, some of which been approved for marketing, indicating that the modulators targeting PPIs have broad prospects. Here, we summarize the recent advances in PPIs modulators, including small molecules, peptides, and antibodies, hoping to provide some guidance to the design of novel drugs targeting PPIs in the future.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Targeting epigenetic protein-protein interactions with small-molecule inhibitors

Epigenetic protein-protein interactions (PPIs) play essential roles in regulating gene expression, and their dysregulations have been implicated in many diseases. These PPIs are comprised of reader domains recognizing post-translational modifications on histone proteins, and of scaffolding proteins that maintain integrities of epigenetic complexes. Targeting PPIs have become focuses for development of small-molecule inhibitors and anticancer therapeutics. Here we summarize efforts to develop small-molecule inhibitors targeting common epigenetic PPI domains. Potent small molecules have been reported for many domains, yet small domains that recognize methylated lysine side chains on histones are challenging in inhibitor development. We posit that the development of potent inhibitors for difficult-to-prosecute epigenetic PPIs may be achieved by interdisciplinary approaches and extensive explorations of chemical space.

Role of protein-protein interactions in allosteric drug design for DNA methyltransferases

DNA methyltransferases (DNMTs) not only play key roles in epigenetic gene regulation, but also serve as emerging targets for several diseases, especially for cancers. Due to the multi-domains of DNMT structures, targeting allosteric sites of protein-protein interactions (PPIs) is becoming an attractive strategy in epigenetic drug discovery. This chapter aims to review the major contemporary approaches utilized for the drug discovery based on PPIs in different dimensions, from the enumeration of allosteric mechanism to the identification of allosteric pockets. These include the construction of protein structure networks (PSNs) based on molecular dynamics (MD) simulations, performing elastic network models (ENMs) and perturbation response scanning (PRS) calculation, the sequence-based conservation and coupling analysis, and the allosteric pockets identification. Furthermore, we complement this methodology by highlighting the role of computational approaches in promising practical applications for the computer-aided drug design, with special focus on two DNMTs, namely, DNMT1 and DNMT3A.

Epigenetic protein complexes: the adequate candidates for the use of a new generation of epidrugs in personalized and precision medicine in cancer

Until recently, drug development in oncology was focused on treating most patients for a specific cancer type without taking in account the heterogeneity between these patients in term of response to treatment. Therefore, this type of broad treatment approach excludes the treatment of patient not responding to disease-specific common drugs. In this review, we focus on the different types of epigenetic drugs currently used as DNA methylation inhibitor agents and their limits in patient care due to their lack of specificity. We also highlight the emergence of a new type of epidrug with higher target specificity due to their original mechanism of action: the disruption of protein complexes involved in the epigenetic modifications.

Protein-protein interaction modulators: advances, successes and remaining challenges

Modulating disease-relevant protein-protein interactions (PPIs) using small-molecule inhibitors is a quite indispensable diagnostic and therapeutic strategy in averting pathophysiological cues and disease progression. Over the years, targeting intracellular PPIs as drug design targets has been a challenging task owing to their highly dynamic and expansive interfacial areas (flat, featureless and relatively large). However, advances in PPI-focused drug discovery technology have been reported and a few drugs are already on the market, with some potential drug-like candidates already in clinical trials. In this article, we review the advances, successes and remaining challenges in the application of small molecules as valuable PPI modulators in disease diagnosis and therapeutics.

N-Methyl D-aspartate receptor subunit signaling in fear extinction

N-Methyl D-aspartate receptors (NMDAR) are central mediators of glutamate actions underlying learning and memory processes including those required for extinction of fear and fear-related behaviors. Consistent with this view, in animal models, antagonists of NMDAR typically impair fear extinction, whereas partial agonists have facilitating effects. Promoting NMDAR function has thus been recognized as a promising strategy towards reduction of fear symptoms in patients suffering from anxiety disorders and post-traumatic disorder (PTSD). Nevertheless, application of these drugs in clinical trials has proved of limited utility. Here we summarize recent advances in our knowledge of NMDAR pharmacology relevant for fear extinction, focusing on molecular, cellular, and circuit aspects of NMDAR function as they relate to fear extinction at the level of behavior and cognition. We also discuss how these advances from animal models might help to understand and overcome the limitations of existing approaches in human anxiety disorders and how novel, more specific, and personalized approaches might help advance future therapeutic strategies.

Inhibitors of DNA Methyltransferases From Natural Sources: A Computational Perspective

Naturally occurring small molecules include a large variety of natural products from different sources that have confirmed activity against epigenetic targets. In this work we review chemoinformatic, molecular modeling, and other computational approaches that have been used to uncover natural products as inhibitors of DNA methyltransferases, a major family of epigenetic targets with therapeutic interest. Examples of computational approaches surveyed in this work are docking, similarity-based virtual screening, and pharmacophore modeling. It is also discussed the chemoinformatic-guided exploration of the chemical space of naturally occurring compounds as epigenetic modulators which may have significant implications in epigenetic drug discovery and nutriepigenetics.

Exploring the chemical space of peptides for drug discovery: a focus on linear and cyclic penta-peptides

Peptide and peptide-like structures are regaining attention in drug discovery. Previous studies suggest that bioactive peptides have diverse structures and may have physicochemical properties attractive to become hit and lead compounds. However, chemoinformatic studies that characterize such diversity are limited. Herein, we report the physicochemical property profile and chemical space of four synthetic linear and cyclic combinatorial peptide libraries. As a case study, the analysis was focused on penta-peptides. The chemical space of the peptide and N-methylated peptides libraries was compared to compound data sets of pharmaceutical relevance. Results indicated that there is a major overlap in the chemical space of N-methylated cyclic peptides with inhibitors of protein-protein interactions and macrocyclic natural products available for screening. Also, there is an overlap between the chemical space of the synthetic peptides with peptides approved for clinical use (or in clinical trials), and to other approved drugs that are outside the traditional chemical space. Results further support that synthetic penta-peptides are suitable compounds to be used in drug discovery projects.

Chemoinformatics: a perspective from an academic setting in Latin America

This perspective discusses the current progress of a chemoinformatics group in a major university in Latin America. Three major aspects are discussed in a critical manner: research, education, and collaboration with industry and other public research networks. It is also presented an overview of the progress in applied research and development of research concepts. Efforts to teach chemoinformatics at the undergraduate and graduate levels are discussed. It is addressed how the partnership with industry and other not-for-profit research institutions not only brings additional sources of funding but, more importantly, increases the impact of the multidisciplinary work and offers the students to be exposed to other research environments. We also discuss the main perspectives and challenges that remain to be addressed in these settings.