2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions

DiPPI: A Curated Data Set for Drug-like Molecules in Protein-Protein Interfaces

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.

Multi-level bioinformatics resources support drug target discovery of protein-protein interactions

Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.

PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods.

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties

Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the "rule of five (RO5)". Therefore, developing PPI-targeted drugs using conventional methods, such as molecular generation models, is challenging. In this study, we propose a molecular generation model based on deep reinforcement learning that is specialized for the production of PPI inhibitors. By introducing a scoring function that can represent the properties of PPI inhibitors, we successfully generated potential PPI inhibitor compounds. These newly constructed virtual compounds possess the desired properties for PPI inhibitors, and they show similarity to commercially available PPI libraries. The virtual compounds are freely available as a virtual library.

Prediction of transient and permanent protein interactions using AI methods

Protein-protein interactions (PPIs) can be classified as permanent or transient interactions based on their stability or lifetime. Understanding the precise details of such protein interactions will pave the way for the discovery of inhibitors and for understanding the nature and function of PPIs. In the present work, 43 relevant physicochemical, geometrical and structural features were calculated for a curated dataset from the literature, comprising of 402 protein-protein complexes of permanent and transient categories, and 5 different Supervised Machine Learning models were developed with Scikit-learn to predict transient and permanent PPI. Additionally, deep learning method with Artificial Neural Network was also performed using Tensor Flow and Keras. Predicted models achieved accuracy ranging from 76.54% to 82.71% and k-NN has achieved the highest accuracy. Detailed analysis of these methods revealed that Interface areas such as Percent interface accessible area, Interface accessible area and Total interface area and the parameters defining the shape of the PPI interface such as Planarity, Eccentricity and Circularity are the most discriminating factors between these two categories. The present method could serve as an effective tool to understand the mechanism of protein association and to predict the transient and permanent interactions, which could supplement the costly and time-consuming experimental techniques.

Using a stacked ensemble learning framework to predict modulators of protein-protein interactions

Identifying small molecule protein-protein interaction modulators (PPIMs) is a highly promising and meaningful research direction for drug discovery, cancer treatment, and other fields. In this study, we developed a stacking ensemble computational framework, SELPPI, based on a genetic algorithm and tree-based machine learning method for effectively predicting new modulators targeting protein-protein interactions. More specifically, extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), random forest (RF), cascade forest, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) were used as basic learners. Seven types of chemical descriptors were taken as the input characteristic parameters. Primary predictions were obtained with each basic learner-descriptor pair. Then, the 6 methods mentioned above were used as meta learners and trained on the primary prediction in turn. The most efficient method was utilized as the meta learner. Finally, the genetic algorithm was used to select the optimal primary prediction output as the input of the meta learner for secondary prediction to obtain the final result. We systematically evaluated our model on the pdCSM-PPI datasets. To our knowledge, our model outperformed all existing models, which demonstrates its great power.

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions

Protein-protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein-protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein-protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions

Protein-protein interactions (PPIs) and protein-metabolite interactions play a key role in many biochemical processes, yet they are often viewed as being independent. However, the fact that small molecule drugs have been successful in inhibiting PPIs suggests a deeper relationship between protein pockets that bind small molecules and PPIs. We demonstrate that 2/3 of PPI interfaces, including antibody-epitope interfaces, contain at least one significant small molecule ligand binding pocket. In a representative library of 50 distinct protein-protein interactions involving hundreds of mutations, >75% of hot spot residues overlap with small molecule ligand binding pockets. Hence, ligand binding pockets play an essential role in PPIs. In representative cases, evolutionary unrelated monomers that are involved in different multimeric interactions yet share the same pocket are predicted to bind the same metabolites/drugs; these results are confirmed by examples in the PDB. Thus, the binding of a metabolite can shift the equilibrium between monomers and multimers. This implicit coupling of PPI equilibria, termed "metabolic entanglement", was successfully employed to suggest novel functional relationships among protein multimers that do not directly interact. Thus, the current work provides an approach to unify metabolomics and protein interactomics.

Structural landscapes of PPI interfaces

Proteins are capable of highly specific interactions and are responsible for a wide range of functions, making them attractive in the pursuit of new therapeutic options. Previous studies focusing on overall geometry of protein-protein interfaces, however, concluded that PPI interfaces were generally flat. More recently, this idea has been challenged by their structural and thermodynamic characterisation, suggesting the existence of concave binding sites that are closer in character to traditional small-molecule binding sites, rather than exhibiting complete flatness. Here, we present a large-scale analysis of binding geometry and physicochemical properties of all protein-protein interfaces available in the Protein Data Bank. In this review, we provide a comprehensive overview of the protein-protein interface landscape, including evidence that even for overall larger, more flat interfaces that utilize discontinuous interacting regions, small and potentially druggable pockets are utilized at binding sites.

Structure-based assessment and druggability classification of protein-protein interaction sites

The featureless interface formed by protein–protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes (i.e. very druggable, druggable, moderately druggable, and difficult). We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface.

pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors

Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other drug-like molecules, mimicking the properties of interacting interfaces. Here, we propose a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions, pdCSM-PPI. This approach was applied to 21 different PPI targets. We developed interaction-specific models that were able to accurately identify active compounds achieving MCC and F1 scores up to 1, and Pearson's correlations up to 0.87, outperforming previous approaches. Using insights from these individual models, we developed a generic protein-protein interaction modulator predictive model, which accurately predicted IC50 with a Pearson's correlation of 0.64 on a low redundancy blind test. Importantly, we were able to accurately identify active from inactive compounds, achieving an AUC of 0.77 and sensitivity and specificity of 76% and 78%, respectively. We believe pdCSM-PPI will be an important tool to help guide more efficient screening of new PPI inhibitors; it is freely available as an easy-to-use web server and API at http://biosig.unimelb.edu.au/pdcsm_ppi.

Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the "Rule-of-Four" (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.

Exploring the chemical space of protein-protein interaction inhibitors through machine learning

Although protein-protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased significantly in recent years. In this study, we analyze the physicochemical properties of 9351 non-redundant inhibitors present in the iPPI-DB and TIMBAL databases to define a computational model for active compounds acting against PPI targets. Principle component analysis (PCA) and k-means clustering were used to identify plausible PPI targets in regions of interest in the active group in the chemical space between active and inactive iPPI compounds. Notably, the uniquely defined active group exhibited distinct differences in activity compared with other active compounds. These results demonstrate that active compounds with regions of interest in the chemical space may be expected to provide insights into potential PPI inhibitors for particular protein targets.

Structural Design and Analysis of the RHOA-ARHGEF1 Binding Mode: Challenges and Applications for Protein-Protein Interface Prediction

The interaction between two proteins may involve local movements, such as small side-chains re-positioning or more global allosteric movements, such as domain rearrangement. We studied how one can build a precise and detailed protein-protein interface using existing protein-protein docking methods, and how it can be possible to enhance the initial structures using molecular dynamics simulations and data-driven human inspection. We present how this strategy was applied to the modeling of RHOA-ARHGEF1 interaction using similar complexes of RHOA bound to other members of the Rho guanine nucleotide exchange factor family for comparative assessment. In parallel, a more crude approach based on structural superimposition and molecular replacement was also assessed. Both models were then successfully refined using molecular dynamics simulations leading to protein structures where the major data from scientific literature could be recovered. We expect that the detailed strategy used in this work will prove useful for other protein-protein interface design. The RHOA-ARHGEF1 interface modeled here will be extremely useful for the design of inhibitors targeting this protein-protein interaction (PPI).

SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2

Small molecule modulators of protein–protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine learning classifiers for prediction of new small molecule modulators of PPI. Our analysis reveals that using random forest (RF) classifier, general PPI Modulators independent of PPI family can be predicted with ROC-AUC higher than 0.9, when training and test sets are generated by random split. The performance of the classifier on data sets very different from those used in training has also been estimated by using different state of the art protocols for removing various types of bias in division of data into training and test sets. The family-specific PPIM predictors developed in this work for 11 clinically important PPI families also have prediction accuracies of above 90% in majority of the cases. All these ML-based predictors have been implemented in a freely available software named SMMPPI for prediction of small molecule modulators for clinically relevant PPIs like RBD:hACE2, Bromodomain_Histone, BCL2-Like_BAX/BAK, LEDGF_IN, LFA_ICAM, MDM2-Like_P53, RAS_SOS1, XIAP_Smac, WDR5_MLL1, KEAP1_NRF2 and CD4_gp120. We have identified novel chemical scaffolds as inhibitors for RBD_hACE PPI involved in host cell entry of SARS-CoV-2. Docking studies for some of the compounds reveal that they can inhibit RBD_hACE2 interaction by high affinity binding to interaction hotspots on RBD. Some of these new scaffolds have also been found in SARS-CoV-2 viral growth inhibitors reported recently; however, it is not known if these molecules inhibit the entry phase.

Crystal structure of a Cu,Zn superoxide dismutase from the thermophilic fungus Chaetomium thermophilum

BACKGROUND

Thermophilic fungi have recently emerged as a promising source of thermostable enzymes. Superoxide dismutases are key antioxidant metalloenzymes with promising therapeutic effects in various diseases, both acute and chronic. However, structural heterogeneity and low thermostability limit their therapeutic efficacy.

OBJECTIVE

Although several studies from hypethermophilic superoxide dismutases (SODs) have been reported, information about Cu,Zn-SODs from thermophilic fungi is scarce. Chaetomium thermophilum is a thermophilic fungus that could provide proteins with thermophilic properties.

METHOD

The enzyme was expressed in Pichia pastoris cells and crystallized using the vapor-diffusion method. X-ray data were collected, and the structure was determined and refined to 1.56 Å resolution. Structural analysis and comparisons were carried out.

RESULTS

The presence of 8 molecules (A through H) in the asymmetric unit resulted in four different interfaces. Molecules A and F form the typical homodimer which is also found in other Cu,Zn-SODs. Zinc was present in all subunits of the structure while copper was found in only four subunits with reduced occupancy (C, D, E and F).

CONCLUSION

The ability of the enzyme to form oligomers and the elevated Thr:Ser ratio may be contributing factors to its thermal stability. Two hydrophobic residues that participate in interface formation and are not present in other CuZn-SODs may play a role in the formation of new interfaces and the oligomerization process. The CtSOD crystal structure reported here is the first Cu,Zn-SOD structure from a thermophilic fungus.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

The iPPI-DB initiative: A Community-centered database of Protein-Protein Interaction modulators

MOTIVATION

One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely-accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets.

RESULTS

Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets, and extended our focus to stabilizers of PPIs as well.

AVAILABILITY AND IMPLEMENTATION

The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Current Challenges and Opportunities in Designing Protein-Protein Interaction Targeted Drugs

It has been noticed that the efficiency of drug development has been decreasing in the past few decades. To overcome the situation, protein-protein interactions (PPIs) have been identified as new drug targets as early as 2000. PPIs are more abundant in human cells than single proteins and play numerous important roles in cellular processes including diseases. However, PPIs have very different physicochemical features from the conventional drug targets, which make targeting PPIs challenging. Therefore, as of now, only a small number of PPI inhibitors have been approved or progressed to a stage of clinical trial. In this article, we first overview previous works that analyzed differences between PPIs with PPI targeting ligands and conventional drugs with their binding pockets. Then, we constructed an up-to-date list of PPI targeting drugs that have been approved or are currently under clinical trial and have bound drug-target structures available. Using the dataset, we analyzed the PPIs and their ligands using several scores of druggability. Druggability scores showed that PPI sites and their drugs targeting PPIs are less druggable than conventional binding pockets and drugs, which also indicates that PPI drugs do not follow the conventional rules for drug design, such as Lipinski's rule of five. Our analyses suggest that developing a new rule would be beneficial for guiding PPI-drug discovery.

Literature mining for context-specific molecular relations using multimodal representations (COMMODAR)

Biological contextual information helps understand various phenomena occurring in the biological systems consisting of complex molecular relations. The construction of context-specific relational resources vastly relies on laborious manual extraction from unstructured literature. In this paper, we propose COMMODAR, a machine learning-based literature mining framework for context-specific molecular relations using multimodal representations. The main idea of COMMODAR is the feature augmentation by the cooperation of multimodal representations for relation extraction. We leveraged biomedical domain knowledge as well as canonical linguistic information for more comprehensive representations of textual sources. The models based on multiple modalities outperformed those solely based on the linguistic modality. We applied COMMODAR to the 14 million PubMed abstracts and extracted 9214 context-specific molecular relations. All corpora, extracted data, evaluation results, and the implementation code are downloadable at https://github.com/jae-hyun-lee/commodar . CCS CONCEPTS: • Computing methodologies~Information extraction • Computing methodologies~Neural networks • Applied computing~Biological networks.

Diameter Is a Key 3D Characteristic for Assessments of Efficient Inhibitors of Protein-Protein Interactions

Three-dimensional (3D) molecular descriptors, including physicochemical and shape properties, for protein-protein interaction (PPI) interface inhibitors have become a topic of discussion. However, the relationships between such properties and binding free energy have not been adequately investigated. In this study, we focused on identifying key 3D molecular descriptors related to the binding free energy and/or the ligand efficiency (LE) of PPI interface inhibitors. A positive correlation was found between the binding free energy and the diameter (D) of cylindrical 3D molecules, in addition to a correlation between LE and D/heavy atom count (HAC). In addition, we showed a correlation between LE and D/HAC for macrocyclic compounds, suggesting that the present findings could be applied during assessments of the potential of macrocyclic PPI interface inhibitors in drug discovery processes.

Targeting the cryptic sites: NMR-based strategy to improve protein druggability by controlling the conformational equilibrium

Cryptic ligand binding sites, which are not evident in the unligated structures, are beneficial in tackling with difficult but attractive drug targets, such as protein-protein interactions (PPIs). However, cryptic sites have thus far not been rationally pursued in the early stages of drug development. Here, we demonstrated by nuclear magnetic resonance that the cryptic site in Bcl-xL exists in a conformational equilibrium between the open and closed conformations under the unligated condition. While the fraction of the open conformation in the unligated wild-type Bcl-xL is estimated to be low, F143W mutation that is distal from the ligand binding site can substantially elevate the population. The F143W mutant showed a higher hit rate in a phage-display peptide screening, and the hit peptide bound to the cryptic site of the wild-type Bcl-xL. Therefore, by controlling the conformational equilibrium in the cryptic site, the opportunity to identify a PPI inhibitor could be improved.

Fr-PPIChem: An Academic Compound Library Dedicated to Protein-Protein Interactions

Protein-protein interactions (PPIs) mediate nearly every cellular process and represent attractive targets for modulating disease states but are challenging to target with small molecules. Despite this, several PPI inhibitors (iPPIs) have entered clinical trials, and a growing number of PPIs have become validated drug targets. However, high-throughput screening efforts still endure low hit rates mainly because of the use of unsuitable screening libraries. Here, we describe the collective effort of a French consortium to build, select, and store in plates a unique chemical library dedicated to the inhibition of PPIs. Using two independent predictive models and two updated databases of experimentally confirmed PPI inhibitors developed by members of the consortium, we built models based on different training sets, molecular descriptors, and machine learning methods. Independent statistical models were used to select putative PPI inhibitors from large commercial compound collections showing great complementarity. Medicinal chemistry filters were applied to remove undesirable structures from this set (such as PAINS, frequent hitters, and toxic compounds) and to improve drug likeness. The remaining compounds were subjected to a clustering procedure to reduce the final size of the library while maintaining its chemical diversity. In practice, the library showed a 46-fold activity rate enhancement when compared to a non-iPPI-enriched diversity library in high-throughput screening against the CD47-SIRPα PPI. The Fr-PPIChem library is plated in 384-well plates and will be distributed on demand to the scientific community as a powerful tool for discovering new chemical probes and early hits for the development of potential therapeutic drugs.

Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server

SUMMARY

Recently we published PROtein binDIng enerGY (PRODIGY), a web-server for the prediction of binding affinity in protein-protein complexes. By using a combination of simple structural properties, such as the residue-contacts made at the interface, PRODIGY has demonstrated a top performance compared with other state-of-the-art predictors in the literature. Here we present an extension of it, named PRODIGY-LIG, aimed at the prediction of affinity in protein-small ligand complexes. The predictive method, properly readapted for small ligand by making use of atomic instead of residue contacts, has been successfully applied for the blind prediction of 102 protein-ligand complexes during the D3R Grand Challenge 2. PRODIGY-LIG has the advantage of being simple, generic and applicable to any kind of protein-ligand complex. It provides an automatic, fast and user-friendly tool ensuring broad accessibility.

AVAILABILITY AND IMPLEMENTATION

PRODIGY-LIG is freely available without registration requirements at http://milou.science.uu.nl/services/PRODIGY-LIG.

The design and development of covalent protein-protein interaction inhibitors for cancer treatment

Protein-protein interactions (PPIs) are central to a variety of biological processes, and their dysfunction is implicated in the pathogenesis of a range of human diseases, including cancer. Hence, the inhibition of PPIs has attracted significant attention in drug discovery. Covalent inhibitors have been reported to achieve high efficiency through forming covalent bonds with cysteine or other nucleophilic residues in the target protein. Evidence suggests that there is a reduced risk for the development of drug resistance against covalent drugs, which is a major challenge in areas such as oncology and infectious diseases. Recent improvements in structural biology and chemical reactivity have enabled the design and development of potent and selective covalent PPI inhibitors. In this review, we will highlight the design and development of therapeutic agents targeting PPIs for cancer therapy.

Biophysical Techniques for Target Validation and Drug Discovery in Transcription-Targeted Therapy

In the post-genome era, pathologies become associated with specific gene expression profiles and defined molecular lesions can be identified. The traditional therapeutic strategy is to block the identified aberrant biochemical activity. However, an attractive alternative could aim at antagonizing key transcriptional events underlying the pathogenesis, thereby blocking the consequences of a disorder, irrespective of the original biochemical nature. This approach, called transcription therapy, is now rendered possible by major advances in biophysical technologies. In the last two decades, techniques have evolved to become key components of drug discovery platforms, within pharmaceutical companies as well as academic laboratories. This review outlines the current biophysical strategies for transcription manipulation and provides examples of successful applications. It also provides insights into the future development of biophysical methods in drug discovery and personalized medicine.

Facilities that make the PDB data collection more powerful

We describe a series of databases and tools that directly or indirectly support biomedical research on macromolecules, with focus on their applicability in protein structure bioinformatics research. DSSP, that determines secondary structures of proteins, has been updated to work well with extremely large structures in multiple formats. The PDBREPORT database that lists anomalies in protein structures has been remade to remove many small problems. These reports are now available as PDF-formatted files with a computer-readable summary. The VASE software has been added to analyze and visualize HSSP multiple sequence alignments for protein structures. The Lists collection of databases has been extended with a series of databases, most noticeably with a database that gives each protein structure a grade for usefulness in protein structure bioinformatics projects. The PDB-REDO collection of reanalyzed and re-refined protein structures that were solved by X-ray crystallography has been improved by dealing better with sugar residues and with hydrogen bonds, and adding many missing surface loops. All academic software underlying these protein structure bioinformatics applications and databases are now publicly accessible, either directly from the authors or from the GitHub software repository.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

BACKGROUND

The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology.

OBJECTIVE

Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions.

METHODS

We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions.

RESULTS

We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs.

CONCLUSION

PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.

Exhaustive Repertoire of Druggable Cavities at Protein-Protein Interfaces of Known Three-Dimensional Structure

Protein-protein interactions (PPIs) offer the unique opportunity to tailor ligands aimed at specifically stabilizing or disrupting the corresponding interfaces and providing a safer alternative to conventional ligands targeting monomeric macromolecules. Selecting biologically relevant protein-protein interfaces for either stabilization or disruption by small molecules is usually biology-driven on a case-by-case basis and does not follow a structural rationale that could be applied to an entire interactome. We herewith provide a first step to the latter goal by using a fully automated and structure-based workflow, applicable to any PPI of known three-dimensional (3D) structure, to identify and prioritize druggable cavities at and nearby PPIs of pharmacological interest. When applied to the entire Protein Data Bank, 164 514 druggable cavities were identified and classified in four groups (interfacial, rim, allosteric, orthosteric) according to their properties and spatial locations. Systematic comparison of PPI cavities with pockets deduced from druggable protein-ligand complexes shows almost no overlap in property space, suggesting that even the most druggable PPI cavities are unlikely to be addressed with conventional drug-like compound libraries. The archive is freely accessible at http://drugdesign.unistra.fr/ppiome .

Why Some Targets Benefit from beyond Rule of Five Drugs

Beyond rule-of-five (bRo5) compounds are increasingly used in drug discovery. Here we analyze 37 target proteins that have bRo5 drugs or clinical candidates. Targets can benefit from bRo5 drugs if they have "complex" hot spot structure with four or more hots spots, including some strong ones. Complex I targets show positive correlation between binding affinity and molecular weight. These targets are conventionally druggable, but reaching additional hot spots enables improved pharmaceutical properties. Complex II targets, mostly protein kinases, also have strong hot spots but show no correlation between affinity and ligand molecular weight, and the primary motivation for creating larger drugs is to increase selectivity. Each target considered as complex III has some specific reason for requiring bRo5 drugs. Finally, targets with "simple" hot spot structure, i.e., three or fewer weak hot spots, must use larger compounds that interact with surfaces beyond the hot spot region to achieve acceptable affinity.

Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery

Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.

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.

Defining Pharmacological Targets by Analysis of Virus-Host Protein Interactions

Viruses are obligate parasites that depend on cellular factors for replication. Pharmacological inhibition of essential viral proteins, mostly enzymes, is an effective therapeutic alternative in the absence of effective vaccines. However, this strategy commonly encounters drug resistance mechanisms that allow these pathogens to evade control. Due to the dependency on host factors for viral replication, pharmacological disruption of the host-pathogen protein-protein interactions (PPIs) is an important therapeutic alternative to block viral replication. In this review we discuss salient aspects of PPIs implicated in viral replication and advances in the development of small molecules that inhibit viral replication through antagonism of these interactions.

The Future of Computational Chemogenomics

Following the elucidation of the human genome, chemogenomics emerged in the beginning of the twenty-first century as an interdisciplinary research field with the aim to accelerate target and drug discovery by making best usage of the genomic data and the data linkable to it. What started as a systematization approach within protein target families now encompasses all types of chemical compounds and gene products. A key objective of chemogenomics is the establishment, extension, analysis, and prediction of a comprehensive SAR matrix which by application will enable further systematization in drug discovery. Herein we outline future perspectives of chemogenomics including the extension to new molecular modalities, or the potential extension beyond the pharma to the agro and nutrition sectors, and the importance for environmental protection. The focus is on computational sciences with potential applications for compound library design, virtual screening, hit assessment, analysis of phenotypic screens, lead finding and optimization, and systems biology-based prediction of toxicology and translational research.

HawkRank: a new scoring function for protein-protein docking based on weighted energy terms

Deciphering the structural determinants of protein–protein interactions (PPIs) is essential to gain a deep understanding of many important biological functions in the living cells. Computational approaches for the structural modeling of PPIs, such as protein–protein docking, are quite needed to complement existing experimental techniques. The reliability of a protein–protein docking method is dependent on the ability of the scoring function to accurately distinguish the near-native binding structures from a huge number of decoys. In this study, we developed HawkRank, a novel scoring function designed for the sampling stage of protein–protein docking by summing the contributions from several energy terms, including van der Waals potentials, electrostatic potentials and desolvation potentials. First, based on the solvation free energies predicted by the Generalized Born model for ~ 800 proteins, a SASA (solvent accessible surface area)-based solvation model was developed, which can give the aqueous solvation free energies for proteins by summing the contributions of 21 atom types. Then, the van der Waals potentials and electrostatic potentials based on the Amber ff14SB force field were computed. Finally, the HawkRank scoring function was derived by determining the most optimal weights for five energy terms based on the training set. Here, MSR (modified success rate), a novel protein–protein scoring quality index, was used to assess the performance of HawkRank and three other popular protein–protein scoring functions, including ZRANK, FireDock and dDFIRE. The results show that HawkRank outperformed the other three scoring functions according to the total number of hits and MSR. HawkRank is available at http://cadd.zju.edu.cn/programs/hawkrank.

Development of Protein-Protein Interaction Inhibitors for the Treatment of Infectious Diseases

Protein-protein interaction (PPI) inhibitors are a rapidly expanding class of therapeutics. Recent advances in our understanding of PPIs and success of early examples of PPI inhibitors demonstrate the feasibility of targeting PPIs. This review summarizes the techniques used for the discovery and optimization of a diverse set PPI inhibitors, focusing on the development of PPI inhibitors as new antibacterial and antiviral agents. We close with a summary of the advances responsible for making PPI inhibitors realistic targets for therapeutic intervention and brief outlook of the field.

Electrostatic interactions between the CTX phage minor coat protein and the bacterial host receptor TolA drive the pathogenic conversion of Vibrio cholerae

Vibrio cholerae is a natural inhabitant of aquatic environments and converts to a pathogen upon infection by a filamentous phage, CTXΦ, that transmits the cholera toxin-encoding genes. This toxigenic conversion of V. cholerae has evident implication in both genome plasticity and epidemic risk, but the early stages of the infection have not been thoroughly studied. CTXΦ transit across the bacterial periplasm requires binding between the minor coat protein named pIII and a bacterial inner-membrane receptor, TolA, which is part of the conserved Tol-Pal molecular motor. To gain insight into the TolA-pIII complex, we developed a bacterial two-hybrid approach, named Oxi-BTH, suited for studying the interactions between disulfide bond-folded proteins in the bacterial cytoplasm of an Escherichia coli reporter strain. We found that two of the four disulfide bonds of pIII are required for its interaction with TolA. By combining Oxi-BTH assays, NMR, and genetic studies, we also demonstrate that two intermolecular salt bridges between TolA and pIII provide the driving forces of the complex interaction. Moreover, we show that TolA residue Arg-325 involved in one of the two salt bridges is critical for proper functioning of the Tol-Pal system. Our results imply that to prevent host evasion, CTXΦ uses an infection strategy that targets a highly conserved protein of Gram-negative bacteria essential for the fitness of V. cholerae in its natural environment.

Identification of an in vivo orally active dual-binding protein-protein interaction inhibitor targeting TNFα through combined in silico/in vitro/in vivo screening

TNFα is a homotrimeric pro-inflammatory cytokine, whose direct targeting by protein biotherapies has been an undeniable success for the treatment of chronic inflammatory diseases. Despite many efforts, no orally active drug targeting TNFα has been identified so far. In the present work, we identified through combined in silico/in vitro/in vivo approaches a TNFα direct inhibitor, compound 1, displaying nanomolar and micromolar range bindings to TNFα. Compound 1 inhibits the binding of TNFα with both its receptors TNFRI and TNFRII. Compound 1 inhibits the TNFα induced apoptosis on L929 cells and the TNFα induced NF-κB activation in HEK cells. In vivo, oral administration of compound 1 displays a significant protection in a murine TNFα-dependent hepatic shock model. This work illustrates the ability of low-cost combined in silico/in vitro/in vivo screening approaches to identify orally available small-molecules targeting challenging protein-protein interactions such as homotrimeric TNFα.

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.

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).

Protein-Protein Interaction Inhibition (2P2I)-Oriented Chemical Library Accelerates Hit Discovery

Protein-protein interactions (PPIs) represent an enormous source of opportunity for therapeutic intervention. We and others have recently pinpointed key rules that will help in identifying the next generation of innovative drugs to tackle this challenging class of targets within the next decade. We used these rules to design an oriented chemical library corresponding to a set of diverse "PPI-like" modulators with cores identified as privileged structures in therapeutics. In this work, we purchased the resulting 1664 structurally diverse compounds and evaluated them on a series of representative protein-protein interfaces with distinct "druggability" potential using homogeneous time-resolved fluorescence (HTRF) technology. For certain PPI classes, analysis of the hit rates revealed up to 100 enrichment factors compared with nonoriented chemical libraries. This observation correlates with the predicted "druggability" of the targets. A specific focus on selectivity profiles, the three-dimensional (3D) molecular modes of action resolved by X-ray crystallography, and the biological activities of identified hits targeting the well-defined "druggable" bromodomains of the bromo and extraterminal (BET) family are presented as a proof-of-concept. Overall, our present study illustrates the potency of machine learning-based oriented chemical libraries to accelerate the identification of hits targeting PPIs. A generalization of this method to a larger set of compounds will accelerate the discovery of original and potent probes for this challenging class of targets.