Small molecules, big targets: drug discovery faces the protein-protein interaction challenge.

Single-Chain Lanthanide Luminescence Biosensors for Cell-Based Imaging and Screening of Protein-Protein Interactions.

Over the past several years, there has been an increasing research effort focused on inhibition of protein-protein interactions (PPIs) to develop novel therapeutic approaches for cancer, including hematologic malignancies. These efforts have led to development of small molecule inhibitors of PPIs, some of which already advanced to the stage of clinical trials while others are at different stages of preclinical optimization, emphasizing PPIs as an emerging and attractive class of drug targets. Here, we review several examples of recently developed inhibitors of PPIs highly relevant to hematologic cancers. We address the existing skepticism about feasibility of targeting PPIs and emphasize potential therapeutic benefit from blocking PPIs in hematologic malignancies. We then use these examples to discuss the approaches for successful identification of PPI inhibitors and provide analysis of the protein-protein interfaces, with the goal to address 'druggability' of new PPIs relevant to hematology. We discuss lessons learned to improve the success of targeting new PPIs and evaluate prospects and limits of the research in this field. We conclude that not all PPIs are equally tractable for blocking by small molecules, and detailed analysis of PPI interfaces is critical for selection of those with the highest chance of success. Together, our analysis uncovers patterns that should help to advance drug discovery in hematologic malignancies by successful targeting of new PPIs.

Homo- and Heterodimerization of Proteins in Cell Signaling: Inhibition and Drug Design.

Protein-protein interactions (PPIs) have been regarded as novel and highly promising drug targets in drug discovery. Numerous new experimental techniques and computational approaches have been developed to assist the design of PPI modulators during the past two decades. However, identification and optimization of small-molecule inhibitors targeting PPIs is still a particularly challenging task due to the "undruggable" profiles of PPI interfaces. Nowadays, in silico screening, especially docking-based virtual screening, has emerged as an effective method to complement experimental high-throughput screening in identifying novel and potent small-molecule PPI inhibitors. Here, on the basis of the 2P2IDB database, we explored the structural features of the known small-molecule PPI inhibitors and analyzed the characteristics of the PPI binding pockets. More importantly, we evaluated the sampling power and screening power of six popular docking programs for PPI targets. Our results indicate that the chlorinated conjugate group and amidelike linkage are two types of privileged fragments of PPI inhibitors; the average druggability of the binding sites of the PPI targets in 2P2IDB is slightly worse than that of traditional ones; both academic and commercial docking programs exhibit an acceptable accuracy on pose prediction for PPI inhibitors, but their screening powers for identifying PPI inhibitors are still not satisfactory. It is expected that our work can provide valuable guidance on the construction of PPI-focused library, the determination of druggable PPI binding pocket, and the selection of docking program for the screening of small-molecule PPI inhibitors.

Protein-protein interactions represent an exciting and challenging target class for therapeutic intervention using small molecules. Protein interaction sites are often devoid of the deep surface pockets presented by "traditional" drug targets, and crystal structures reveal that inhibitors typically engage these sites using very shallow binding modes. As a consequence, modern virtual screening tools developed to identify inhibitors of traditional drug targets do not perform as well when they are instead deployed at protein interaction sites. To address the need for novel inhibitors of important protein interactions, here we introduce an alternate docking strategy specifically designed for this regime. Our method, termed DARC (Docking Approach using Ray-Casting), matches the topography of a surface pocket "observed" from within the protein to the topography "observed" when viewing a potential ligand from the same vantage point. We applied DARC to carry out a virtual screen against the protein interaction site of human antiapoptotic protein Mcl-1 and found that four of the top-scoring 21 compounds showed clear inhibition in a biochemical assay. The Ki values for these compounds ranged from 1.2 to 21 μM, and each had ligand efficiency comparable to promising small-molecule inhibitors of other protein-protein interactions. These hit compounds do not resemble the natural (protein) binding partner of Mcl-1, nor do they resemble any known inhibitors of Mcl-1. Our results thus demonstrate the utility of DARC for identifying novel inhibitors of protein-protein interactions.

It is becoming increasingly clear that small molecules can often act as effective protein-protein interaction (PPI) inhibitors, an area of increasing interest for its many possible therapeutic applications. We have identified several organic dyes and related small molecules that (i) concentration-dependently inhibit the important CD40-CD154 costimulatory interaction with activities in the low micromolar (microM) range, (ii) show selectivity toward this particular PPI, (iii) seem to bind on the surface of CD154, and (iv) concentration-dependently inhibit the CD154-induced B cell proliferation. They were identified through an iterative activity screening/structural similarity search procedure starting with suramin as lead, and the best smaller compounds, the main focus of the present work, achieved an almost 3-fold increase in ligand efficiency (DeltaG(0)/nonhydrogen atom = 0.8 kJ/N(nHa)) approaching the average of known promising small-molecule PPI inhibitors (approximately 1.0 kJ/N(nHa)). Since CD154 is a member of the tumor necrosis factor (TNF) superfamily of cell surface interaction molecules, inhibitory activities on the TNF-R1-TNF-alpha interactions were also determined to test for specificity, and the compounds selected here all showed more than 30-fold selectivity toward the CD40-CD154 interaction. Because of their easy availability in various structural scaffolds and because of their good protein-binding ability, often explored for tissue-specific staining and other purposes, such organic dyes can provide a valuable addition to the chemical space searched to identify small molecule PPI inhibitors in general.

Peptides based on the amino acid sequences found at protein-protein interaction sites make excellent leads for antagonist development. A statistical picture of amino acids involved in protein-protein interactions indicates that proteins recognize and interact with one another through the restricted set of specialized interface amino acid residues, Pro, Ile, Tyr, Trp, Asp and Arg. These amino acids represent residues from each of the three classes of amino acids, hydrophobic, aromatic and charged, with one anionic and one cationic residue at neutral pH. The use of peptides as drug leads has been successfully used to search for antagonists of cell-surface receptors. Peptide, peptidomimetic, and non-peptide organic inhibitors of a class of cell surface receptors, the integrins, currently serve as therapeutic and diagnostic imaging agents. In this review, we discuss the structural features of protein-protein interactions as well as the design of peptides, peptidomimetics, and small organic molecules for the inhibition of protein-protein interactions. Information gained from studying inhibitors of integrin functions is now being applied to the design and testing of inhibitors of other protein-protein interactions. Most drug development progress in the past several decades has been made using the enzyme binding-pocket model of drug targets. Small molecules are designed to fit into the substrate-binding pockets of proteins based on a lock-and-key, induced-fit, or conformational ensemble model of the protein binding site. Traditionally, enzymes have been used as therapeutic drug targets because it was easier to develop rapid, sensitive screening assays, and to find low molecular weight inhibitors that blocked the active site. However, for proteins which interact with other proteins, rather than with small substrate molecules, the lack of binding pockets means that this approach will not generally succeed. There exist many diseases in which the inhibition of protein-protein interactions would provide therapeutic benefit, but there are no general methods available to address such problems. The focus of the first part of this review is to discuss the features of protein-protein interactions which may serve as general guidelines for the development and design of inhibitors for protein-protein interactions. In the second part we focus on the design of peptides (lead compounds) and their conversion into peptidomimetics or small organic molecules for the inhibition of protein-protein interactions. We draw examples from the important and emerging area of integrin-based cell adhesion and show how the principles of protein-protein interactions are followed in the discovery, optimization and usage of specific protein interface peptides as drug leads.

Over the last two decades, there have been extensive efforts to develop small-molecule inhibitors of protein-protein interactions (PPIs) as novel therapeutics for cancer, including hematologic malignancies. Despite the numerous challenges associated with developing PPI inhibitors, a significant number of them have advanced to clinical studies in hematologic patients in recent years. The US Food and Drug Administration approval of the very first PPI inhibitor, venetoclax, demonstrated the real clinical value of blocking protein-protein interfaces. In this review, we discuss the most successful examples of PPI inhibitors that have reached clinical studies in patients with hematologic malignancies. We also describe the challenges of blocking PPIs with small molecules, clinical resistance to such compounds, and the lessons learned from the development of successful PPI inhibitors. Overall, this review highlights the remarkable success and substantial promise of blocking PPIs in hematologic malignancies.

Protein-protein interactions (PPIs) have been regarded as an attractive emerging class of therapeutic targets for the development of new treatments. Computational approaches, especially molecular docking, have been extensively employed to predict the binding structures of PPI-inhibitors or discover novel small molecule PPI inhibitors. However, due to the relatively 'undruggable' features of PPI interfaces, accurate predictions of the binding structures for ligands towards PPI targets are quite challenging for most docking algorithms. Here, we constructed a non-redundant pose ranking benchmark dataset for small-molecule PPI inhibitors, which contains 900 binding poses for 184 protein-ligand complexes. Then, we evaluated the performance of MM/PB(GB)SA approaches to identify the correct binding poses for PPI inhibitors, including two Prime MM/GBSA procedures from the Schrödinger suite and seven different MM/PB(GB)SA procedures from the Amber package. Our results showed that MM/PBSA outperformed the Glide SP scoring function (success rate of 58.6%) and MM/GBSA in most cases, especially the PB3 procedure which could achieve an overall success rate of ∼74%. Moreover, the GB6 procedure (success rate of 68.9%) performed much better than the other MM/GBSA procedures, highlighting the excellent potential of the GBNSR6 implicit solvation model for pose ranking. Finally, we developed the webserver of Fast Amber Rescoring for PPI Inhibitors (farPPI), which offers a freely available service to rescore the docking poses for PPI inhibitors by using the MM/PB(GB)SA methods. farPPI web server is freely available at http://cadd.zju.edu.cn/farppi/. Supplementary data are available at Bioinformatics online.

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

Pharmacophore searches have become a popular tool for virtual screening of libraries to identify novel active substances that can be potentially developed into drugs. While they have been applied for years on common drug targets, their application in the discovery of protein-protein interaction inhibitors remains limited. This review describes current pharmacophore modelling methods applied in the discovery of novel inhibitors targeting protein-protein interactions. We first address the mimicry of protein-protein interactions with their respective inhibitors as observed in crystal structure complexes. This mimicry can be exploited to derive a pharmacophore query from protein-protein complex structures. We then discuss several cases where pharmacophore queries were utilized for the discovery of first-in-class inhibitors of their respective protein-protein interaction targets. These examples have demonstrated the usefulness of pharmacophore modelling in the quest for protein-protein interaction inhibitors.

Inhibitors of protein–protein interactions (PPIs): an analysis of scaffold choices and buried surface area

Targeting protein–protein interactions (PPIs) for therapeutic interventions has been an attractive strategy in drug discovery. Because drug development for enzyme targets has been limited by the difficulties in achieving sufficient specificity, PPI inhibitors with extremely high specificity have recently attracted considerable attention. Despite advances in various techniques, such as nuclear magnetic resonance (NMR), surface plasma resonance (SPR), and fluorescence polarization (FP), the high-throughput screening (HTS) of small-molecule PPI inhibitors is highly challenging owing to several critical limitations: i) the large amount (mg quantities) of sample required for NMR; ii) the low sensitivity of SPR for detection of small-molecule binding to proteins; and iii) the requirement for fluorophore labeling in FP. Hence, there is a need to develop a robust HTS methodology to facilitate the discovery of small molecule drugs against PPI targets. Nanopore sensors have several unique advantages compared with more conventional techniques, including single-molecule resolution and ultrasensitivity, label-free and real-time measurements, and high-throughput detection. Although nanopores have been used to characterize the biophysical properties of diverse biomolecules, including DNA, RNA, and proteins, they have never been applied to the screening of small-molecule drugs such as PPI inhibitors. Blocking the interaction between mouse double minute 2 (MDM2) and p53 transactivation domain (p53TAD) has been an attractive strategy for cancer therapy because it can restore p53 function, resulting in cancer cell apoptosis. Using this therapeutic strategy, many p53TAD-mimetic lead compounds have been identified for cancer treatment. Among them, Nutlin-3 is one of the most potent MDM2 antagonists and acts as a competitive inhibitor of the MDM2/p53TAD interaction (Kd=0.1 μM). Nutlin-3 structurally mimics the 15-residue a-helical p53TAD peptide that binds to MDM2 (Kd=0.6μM). To probe the MDM2/p53TAD interaction and its small molecule inhibition using solid-state nanopores, we fabricated ~10–15 nm-sized nanopores in low-pressure chemical vapor deposition (LPCVD) SiNx membranes transferred to the Pyrex substrates. The structure of the N-terminal p53TAD binding domain of MDM2 (PDB code: 1YCR) showed dimensions of 3.1 nm × 3.5 nm × 4.2 nm. At the applied voltage of -175 mV across the nanopore, the positively charged MDM2 domain (residues 3–109, MW=12.3 kDa, net charge at pH 7.4=+2.9e, pI=9.02) was electrophoretically driven from one chamber toward the negative electrode in the other chamber. The passage of MDM2 through the nanopore gave rise to a temporary reduction in the ionic current, leading to the detection of MDM2 translocation events at the single-molecule level. In contrast to the vigorous translocation of free MDM2, the number of translocation events for the MDM2/GST-p53TAD complex through the nanopore was dramatically reduced to a negligible level. Upon complex formation, the net charge of the proteins at pH 7.4 changes from +2.9e (in free MDM2) to -13.7e (in the MDM2/GST-p53TAD complex) owing to charge masking of MDM2 by negatively charged GST-p53TAD. As a result, the overall negatively charged protein complex could not translocate through the nanopore at the applied negative voltage. We measured the nanopore translocation of the MDM2/GST-p53TAD complex in the presence of Nutlin-3. After the addition of Nutlin-3 to the protein complex, the translocation of free MDM2 was almost recovered, which indicated that the disruption of the MDM2/GST-p53TAD interaction by Nutlin-3 liberated MDM2 from the GST-p53TAD-bound complex. To test whether the MDM2/GST-p53TAD interaction is indeed specifically inhibited by Nutlin-3, we repeated the nanopore experiment with a negative control, ABT-737, which is an inhibitor of Bcl-2 family proteins and does not bind to MDM2. Unlike Nutlin-3, ABT-737 could not recover the translocation of MDM2, confirming that Nutlin-3 specifically blocked the interaction between MDM2 and GST-p53TAD. We believe that this nanopore-based drug screening platform will provide a remarkable improvement over current technological limitations in drug discovery at protein-protein interfaces.

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

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com.