Druggability assessment of protein-protein interfaces

KK-LC-1 as a therapeutic target to eliminate ALDH+ stem cells in triple negative breast cancer

Failure to achieve complete elimination of triple negative breast cancer (TNBC) stem cells after adjuvant therapy is associated with poor outcomes. Aldehyde dehydrogenase 1 (ALDH1) is a marker of breast cancer stem cells (BCSCs), and its enzymatic activity regulates tumor stemness. Identifying upstream targets to control ALDH⁺ cells may facilitate TNBC tumor suppression. Here, we show that KK-LC-1 determines the stemness of TNBC ALDH⁺ cells via binding with FAT1 and subsequently promoting its ubiquitination and degradation. This compromises the Hippo pathway and leads to nuclear translocation of YAP1 and ALDH1A1 transcription. These findings identify the KK-LC-1-FAT1-Hippo-ALDH1A1 pathway in TNBC ALDH⁺ cells as a therapeutic target. To reverse the malignancy due to KK-LC-1 expression, we employ a computational approach and discover Z839878730 (Z8) as an small-molecule inhibitor which may disrupt KK-LC-1 and FAT1 binding. We demonstrate that Z8 suppresses TNBC tumor growth via a mechanism that reactivates the Hippo pathway and decreases TNBC ALDH⁺ cell stemness and viability.

MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking-the so-called scoring problem-still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein-protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein-protein interfacial features and by using ensemble methods to combine multiple scoring functions.

Discovery of novel druggable pockets on polyomavirus VP1 through crystallographic fragment-based screening to develop capsid assembly inhibitors

Polyomaviruses are a family of ubiquitous double-stranded DNA viruses many of which are human pathogens. These include BK polyomavirus which causes severe urinary tract infection in immunocompromised patients and Merkel cell polyomavirus associated with aggressive cancers. The small genome of polyomaviruses lacks conventional drug targets, and no specific drugs are available at present. Here we focus on the main structural protein VP1 of BK polyomavirus which is responsible for icosahedral capsid formation. To provide a foundation towards rational drug design, we crystallized truncated VP1 pentamers and subjected them to a high-throughput screening for binding drug-like fragments through a direct X-ray analysis. To enable a highly performant screening, rigorous optimization of the crystallographic pipeline and processing with the latest generation PanDDA2 software were necessary. As a result, a total of 144 binding hits were established. Importantly, the hits are well clustered in six surface pockets. Three pockets are located on the outside of the pentamer and map on the regions where the 'invading' C-terminal arm of another pentamer is attached upon capsid assembly. Another set of three pockets is situated within the wide pore along the five-fold axis of the VP1 pentamer. These pockets are situated at the interaction interface with the minor capsid protein VP2 which is indispensable for normal functioning of the virus. Here we systematically analyse the three outside pockets which are highly conserved across various polyomaviruses, while point mutations in these pockets are detrimental for viral replication. We show that one of the pockets can accommodate antipsychotic drug trifluoperazine. For each pocket, we derive pharmacophore features which enable the design of small molecules preventing the interaction between VP1 pentamers and therefore inhibiting capsid assembly. Our data lay a foundation towards a rational development of first-in-class drugs targeting polyomavirus capsid.

Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We?

Peptides and peptidomimetics are strongly re-emerging as amenable candidates in the development of therapeutic strategies against a plethora of pathologies. In particular, these molecules are extremely suitable to treat diseases in which a major role is played by protein-protein interactions (PPIs). Unlike small organic compounds, peptides display both a high degree of specificity avoiding secondary off-targets effects and a relatively low degree of toxicity. Further advantages are provided by the possibility to easily conjugate peptides to functionalized nanoparticles, so improving their delivery and cellular uptake. In many cases, such molecules need to assume a specific three-dimensional conformation that resembles the bioactive one of the endogenous ligand. To this end, chemical modifications are introduced in the polypeptide chain to constrain it in a well-defined conformation, and to improve the drug-like properties. In this context, a successful strategy for peptide/peptidomimetics design and optimization is to combine different computational approaches ranging from structural bioinformatics to atomistic simulations. Here, we review the computational tools for peptide design, highlighting their main features and differences, and discuss selected protocols, among the large number of methods available, used to assess and improve the stability of the functional folding of the peptides. Finally, we introduce the simulation techniques employed to predict the binding affinity of the designed peptides for their targets.

Emerging Screening Approaches in the Development of Nrf2-Keap1 Protein-Protein Interaction Inhibitors

Due to role of the Keap1–Nrf2 protein–protein interaction (PPI) in protecting cells from oxidative stress, the development of small molecule inhibitors that inhibit this interaction has arisen as a viable approach to combat maladies caused by oxidative stress, such as cancers, neurodegenerative disease and diabetes. To obtain specific and genuine Keap1–Nrf2 inhibitors, many efforts have been made towards developing new screening approaches. However, there is no inhibitor for this target entering the clinic for the treatment of human diseases. New strategies to identify novel bioactive compounds from large molecular databases and accelerate the developmental process of the clinical application of Keap1–Nrf2 protein–protein interaction inhibitors are greatly needed. In this review, we have summarized virtual screening and other methods for discovering new lead compounds against the Keap1–Nrf2 protein–protein interaction. We also discuss the advantages and limitations of different strategies, and the potential of this PPI as a drug target in disease therapy.

The IKK-binding domain of NEMO is an irregular coiled coil with a dynamic binding interface

NEMO is an essential component in the activation of the canonical NF-κB pathway and exerts its function by recruiting the IκB kinases (IKK) to the IKK complex. Inhibition of the NEMO/IKKs interaction is an attractive therapeutic paradigm for diseases related to NF-κB mis-regulation, but a difficult endeavor because of the extensive protein-protein interface. Here we report the high-resolution structure of the unbound IKKβ-binding domain of NEMO that will greatly facilitate the design of NEMO/IKK inhibitors. The structures of unbound NEMO show a closed conformation that partially occludes the three binding hot-spots and suggest a facile transition to an open state that can accommodate ligand binding. By fusing coiled-coil adaptors to the IKKβ-binding domain of NEMO, we succeeded in creating a protein with improved solution behavior, IKKβ-binding affinity and crystallization compatibility, which will enable the structural characterization of new NEMO/inhibitor complexes.

In Silico Target Druggability Assessment: From Structural to Systemic Approaches

This chapter will focus on today's in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

The modulation of PPIs by low molecular weight chemical compounds, particularly by orally bioavailable molecules, would be very valuable in numerous disease indications. However, it is known that PPI inhibitors (iPPIs) tend to have properties that are linked to poor Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and in some cases to poor clinical outcomes. Previously reported in silico analyses of iPPIs have essentially focused on physicochemical properties but several other ADMET parameters would be important to assess. In order to gain new insights into the ADMET properties of iPPIs, computations were carried out on eight datasets collected from several databases. These datasets involve compounds targeting enzymes, GPCRs, ion channels, nuclear receptors, allosteric modulators, oral marketed drugs, oral natural product-derived marketed drugs and iPPIs. Several trends are reported that should assist the design and optimization of future PPI inhibitors, either for drug discovery endeavors or for chemical biology projects.

Small molecules inhibiting Keap1-Nrf2 protein-protein interactions: a novel approach to activate Nrf2 function

Oxidative stress is well recognized to contribute to the cause of a wide range of diseases, such as cancer, diabetes, Alzheimer's disease, arteriosclerosis, and inflammation. The Keap1-Nrf2-ARE pathway plays a critical regulatory role and can protect cells from oxidative stress through activating Nrf2 to induce its downstream phase II enzymes. Nrf2 activation through the covalent inactivation of Keap1 may cause unpredictable side effects. Non-covalent disruption of the Keap1-Nrf2 protein-protein interactions is an alternative strategy for Nrf2 activation, potentially with reduced risk of toxicity. Efforts have been made in recent years to develop peptide- and small molecule-based Keap1-Nrf2 PPI inhibitors via different approaches, including high-throughput screening, target-based virtual screening, structure-based optimization, and fragment-based drug design. This review aims to highlight the recently discovered small-molecule inhibitors as well as their therapeutic potential.

A two-step binding mechanism for the self-binding peptide recognition of target domains

By using state-of-the-art molecular dynamics to reconstruct the complete structural dynamics picture of self-binding peptides, a two-step binding mechanism was proposed, including a fast, nonspecific diffusive phase and a slow, specific organizational phase.

State-of-the-art strategies for targeting protein-protein interactions by small-molecule inhibitors

Targeting protein-protein interactions (PPIs) has emerged as a viable approach in modern drug discovery. However, the identification of small molecules enabling us to effectively interrupt their interactions presents significant challenges. In the recent past, significant advances have been made in the development of new biological and chemical strategies to facilitate the discovery process of small-molecule PPI inhibitors. This review aims to highlight the state-of-the-art technologies and the achievements made recently in this field. The "hot spots" of PPIs have been proved to be critical for small molecules to bind. Three strategies including screening, designing, and synthetic approaches have been explored for discovering PPI inhibitors by targeting the "hot spots". Although the classic high throughput screening approach can be used, fragment screening, fragment-based drug design and newly improved virtual screening are demonstrated to be more effective in the discovery of PPI inhibitors. In addition to screening approaches, design strategies including anchor-based and small molecule mimetics of secondary structures involved in PPIs have become powerful tools as well. Finally, constructing new chemically spaced libraries with high diversity and complexity is becoming an important area of interest for PPI inhibitors. The successful cases from the recent five year studies are used to illustrate how these approaches are implemented to uncover and optimize small molecule PPI inhibitors and notably some of them have become promising therapeutics.

Targeting protein-protein interactions (PPIs) of transcription factors: Challenges of intrinsically disordered proteins (IDPs) and regions (IDRs)

In this review we discuss recent progress in targeting the protein-protein interactions made by oncogenic transcription factors. We particularly focus on the challenges posed by the prevalence of intrinsically disordered regions in this class of protein and the strategies being used to overcome them.

Mitigating risk in academic preclinical drug discovery

The number of academic drug discovery centres has grown considerably in recent years, providing new opportunities to couple the curiosity-driven research culture in academia with rigorous preclinical drug discovery practices used in industry. To fully realize the potential of these opportunities, it is important that academic researchers understand the risks inherent in preclinical drug discovery, and that translational research programmes are effectively organized and supported at an institutional level. In this article, we discuss strategies to mitigate risks in several key aspects of preclinical drug discovery at academic drug discovery centres, including organization, target selection, assay design, medicinal chemistry and preclinical pharmacology.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Targeting protein-protein interactions for drug discovery

Protein-protein interactions are associated with key activities and pathways in the cell, and in that regard are promising targets for drug discovery. However, in terms of small molecule drugs, this promise has not been realized. The physical nature of many protein-protein interaction surfaces renders them unable to support binding of small drug-like molecules. In addition, there are other unique hurdles presented by this class that make the drug development process difficult and risky. Nevertheless, success stories have begun to steadily appear in this field. These experiences are starting to provide general strategies and tools to help overcome the problems inherent in pursuing protein-protein interaction targets. These lessons should improve the rate of success as these systems are pursued in the future.

Disulfide-mediated stabilization of the IκB kinase binding domain of NF-κB essential modulator (NEMO)

Human NEMO (NF-κB essential modulator) is a 419 residue scaffolding protein that, together with catalytic subunits IKKα and IKKβ, forms the IκB kinase (IKK) complex, a key regulator of NF-κB pathway signaling. NEMO is an elongated homodimer comprising mostly α-helix. It has been shown that a NEMO fragment spanning residues 44–111, which contains the IKKα/β binding site, is structurally disordered in the absence of bound IKKβ. Herein we show that enforcing dimerization of NEMO_1–120 or NEMO_44–111 constructs through introduction of one or two interchain disulfide bonds, through oxidation of the native Cys54 residue and/or at position 107 through a Leu107Cys mutation, induces a stable α-helical coiled-coil structure that is preorganized to bind IKKβ with high affinity. Chemical and thermal denaturation studies showed that, in the context of a covalent dimer, the ordered structure was stabilized relative to the denatured state by up to 3 kcal/mol. A full-length NEMO-L107C protein formed covalent dimers upon treatment of mammalian cells with H₂O₂. Furthermore, NEMO-L107C bound endogenous IKKβ in A293T cells, reconstituted TNF-induced NF-κB signaling in NEMO-deficient cells, and interacted with TRAF6. Our results indicate that the IKKβ binding domain of NEMO possesses an ordered structure in the unbound state, provided that it is constrained within a dimer as is the case in the constitutively dimeric full-length NEMO protein. The stability of the NEMO coiled coil is maintained by strong interhelix interactions in the region centered on residue 54. The disulfide-linked constructs we describe herein may be useful for crystallization of NEMO’s IKKβ binding domain in the absence of bound IKKβ, thereby facilitating the structural characterization of small-molecule inhibitors.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Focused chemical libraries--design and enrichment: an example of protein-protein interaction chemical space

One of the many obstacles in the development of new drugs lies in the limited number of therapeutic targets and in the quality of screening collections of compounds. In this review, we present general strategies for building target-focused chemical libraries with a particular emphasis on protein-protein interactions (PPIs). We describe the chemical spaces spanned by nine commercially available PPI-focused libraries and compare them to our 2P2I3D academic library, dedicated to orthosteric PPI modulators. We show that although PPI-focused libraries have been designed using different strategies, they share common subspaces. PPI inhibitors are larger and more hydrophobic than standard drugs; however, an effort has been made to improve the drug-likeness of focused chemical libraries dedicated to this challenging class of targets.

2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine

Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this 'high-hanging fruit' is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database (http://2p2idb.cnrs-mrs.fr/) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard Dragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns. This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

The scoring of poses in protein-protein docking: current capabilities and future directions

BACKGROUND

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.

RESULTS

We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically.

CONCLUSIONS

All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

Plucking the high hanging fruit: a systematic approach for targeting protein-protein interactions

Development of specific ligands for protein targets that help decode the complexities of protein-protein interaction networks is a key goal for the field of chemical biology. Despite the emergence of powerful in silico and experimental high-throughput screening strategies, the discovery of synthetic ligands that selectively modulate protein-protein interactions remains a challenge for bioorganic and medicinal chemists. This Perspective discusses emerging principles for the rational design of PPI inhibitors. Fundamentally, the approach seeks to adapt nature's protein recognition principles for the design of suitable secondary structure mimetics.

One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade

Online resources enabling and supporting drug discovery have blossomed during the past ten years. However, drug hunters commonly find themselves overwhelmed by the proliferation of these computer-based resources. Ten years ago, we, the authors of this review, felt that a comprehensive list of in silico resources relating to drug discovery was needed. Especially because the internet provides a wealth of inspiring tools that, if fully exploited, could greatly assist the process. We present here a compilation of online tools and databases collected over the past decade. The tools were essentially found through literature and internet searches and, currently, our list contains over 1500 URLs. We also briefly highlight some recently reported services and comment about ongoing and future efforts in the field.

Introducing Bayesian thinking to high-throughput screening for false-negative rate estimation

High-throughput screening (HTS) has been widely used to identify active compounds (hits) that bind to biological targets. Because of cost concerns, the comprehensive screening of millions of compounds is typically conducted without replication. Real hits that fail to exhibit measurable activity in the primary screen due to random experimental errors will be lost as false-negatives. Conceivably, the projected false-negative rate is a parameter that reflects screening quality. Furthermore, it can be used to guide the selection of optimal numbers of compounds for hit confirmation. Therefore, a method that predicts false-negative rates from the primary screening data is extremely valuable. In this article, we describe the implementation of a pilot screen on a representative fraction (1%) of the screening library in order to obtain information about assay variability as well as a preliminary hit activity distribution profile. Using this training data set, we then developed an algorithm based on Bayesian logic and Monte Carlo simulation to estimate the number of true active compounds and potential missed hits from the full library screen. We have applied this strategy to five screening projects. The results demonstrate that this method produces useful predictions on the numbers of false negatives.

iPPI-DB: a manually curated and interactive database of small non-peptide inhibitors of protein-protein interactions

The development of small molecule drugs targeting protein-protein interactions (PPI) represents a major challenge, in part owing to the misunderstanding of the PPI chemical space. To this end, we have manually collected the structures, the physicochemical and pharmacological profiles of 1650 PPI inhibitors across 13 families of PPI targets in a database named iPPI-DB. To access iPPI-DB, we propose a user-friendly web application (www.ippidb.cdithem.fr) with customizable queries and intuitive visualizing functionalities for associated properties of the compounds. This could assist scientists to design the next generation of PPI drugs. In this review, we describe iPPI-DB in the context of other low molecular weight molecule databases.

Structure-based target druggability assessment

The focus of this chapter is on the important concepts behind the in silico techniques that are used today to assess target druggability. The first step of the assessment consists of finding cavity space in the protein using 2D and/or 3D topological concepts. These concepts underlie the geometry and energy-based pocketfinder algorithms. Analysis pursues on the physico-chemical complementarity between the binding site and the drug like molecule. Geometrical and molecular flexibility aspect are also included in this assessment. The presence of hot interaction spots are shown to be particularly important for targeting protein-protein interactions. Finally, binding site promiscuity can be assessed by large scale structural comparison with other targets. Common chemical features amongst protein cavities can predict potential cross-reactivity with unwanted targets.

Pocket-based drug design: exploring pocket space

The identification and application of druggable pockets of targets play a key role in in silico drug design, which is a fundamental step in structure-based drug design. Herein, some recent progresses and developments of the computational analysis of pockets have been covered. Also, the pockets at the protein-protein interfaces (PPI) have been considered to further explore the pocket space for drug discovery. We have presented two case studies targeting the kinetic pockets generated by normal mode analysis and molecular dynamics method, respectively, in which we focus upon incorporating the pocket flexibility into the two-dimensional virtual screening with both affinity and specificity. We applied the specificity and affinity (SPA) score to quantitatively estimate affinity and evaluate specificity using the intrinsic specificity ratio (ISR) as a quantitative criterion. In one of two cases, we also included some applications of pockets located at the dimer interfaces to emphasize the role of PPI in drug discovery. This review will attempt to summarize the current status of this pocket issue and will present some prospective avenues of further inquiry.

Perspectives on the discovery of small-molecule modulators for epigenetic processes

Epigenetic gene regulation is a critical process controlling differentiation and development, the malfunction of which may underpin a variety of diseases. In this article, we review the current landscape of small-molecule epigenetic modulators including drugs on the market, key compounds in clinical trials, and chemical probes being used in epigenetic mechanistic studies. Hit identification strategies for the discovery of small-molecule epigenetic modulators are summarized with respect to writers, erasers, and readers of histone marks. Perspectives are provided on opportunities for new hit discovery approaches, some of which may define the next generation of therapeutic intervention strategies for epigenetic processes.