Targeting Unoccupied Surfaces on Protein-Protein Interfaces

Subpocket-Based Analysis Approach for the Protein Pocket Dynamics

Structural and dynamic characteristics of protein pockets remarkably influence their biological functions and are also important for enzyme engineering and new drug research and development. To date, several softwares have been developed to analyze the dynamic properties of protein pockets. However, due to the complexity and diversity of the pocket information during the kinetic relaxation, further improvement and capacity expansion of current tools are required. Here, we developed a platform software AlphaTraj in which a computational strategy that divides the whole protein pocket into subpockets and examines various properties of the subpockets such as survival time, stability, and correlation was proposed and implemented. We also proposed a scoring function for the subpockets as well as the whole pocket to visualize the quality of the pocket. Furthermore, we implemented automated conformational search functions for ligand docking and ligand optimization. These functions may help us to gain a deep understanding of the dynamic properties of protein pockets and accelerate the protein engineering and the design of inhibitors and small-molecule drugs. The software is freely available at https://github.com/dooo12332/AlphaTraj.git under the GNU GPL license.

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.

What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks

Identifying residues critical to protein-protein binding and efficient design of stable and specific protein binders are challenging tasks. Extending beyond the direct contacts in a protein-protein binding interface, our study employs computational modeling to reveal the essential network of residue interactions and dihedral angle correlations critical in protein-protein recognition. We hypothesized that mutating residues exhibiting highly correlated dynamic motion within the interaction network could efficiently optimize protein-protein interactions to create tight and selective protein binders. We tested this hypothesis using the ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complex, since Ub is a central player in multiple cellular functions and PLpro is an antiviral drug target. Our designed ubiquitin variant (UbV) hosting three mutated residues displayed a ∼3,500-fold increase in functional inhibition relative to wild-type Ub. Further optimization of two C-terminal residues within the Ub network resulted in a KD of 1.5 nM and IC50 of 9.7 nM for the five-point Ub mutant, eliciting 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study highlights residue correlation and interaction networks in protein-protein interactions, and introduces an effective approach to design high-affinity protein binders for cell biology research and future therapeutics.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

Designing Cyclic-Constrained Peptides to Inhibit Human Phosphoglycerate Dehydrogenase

Although loop epitopes at protein-protein binding interfaces often play key roles in mediating oligomer formation and interaction specificity, their binding sites are underexplored as drug targets owing to their high flexibility, relatively few hot spots, and solvent accessibility. Prior attempts to develop molecules that mimic loop epitopes to disrupt protein oligomers have had limited success. In this study, we used structure-based approaches to design and optimize cyclic-constrained peptides based on loop epitopes at the human phosphoglycerate dehydrogenase (PHGDH) dimer interface, which is an obligate homo-dimer with activity strongly dependent on the oligomeric state. The experimental validations showed that these cyclic peptides inhibit PHGDH activity by directly binding to the dimer interface and disrupting the obligate homo-oligomer formation. Our results demonstrate that loop epitope derived cyclic peptides with rationally designed affinity-enhancing substitutions can modulate obligate protein homo-oligomers, which can be used to design peptide inhibitors for other seemingly intractable oligomeric proteins.

Rational peptide design for inhibition of the KIX-MLL interaction

The kinase-inducible domain interacting (KIX) domain is an integral part of the general transcriptional coactivator CREB-binding protein, and has been associated with leukemia, cancer, and various viral diseases. Hence, the KIX domain has attracted considerable attention in drug discovery and development. Here, we rationally designed a KIX inhibitor using a peptide fragment corresponding to the transactivation domain (TAD) of the transcriptional activator, mixed-lineage leukemia protein (MLL). We performed theoretical saturation mutagenesis using the Rosetta software to search for mutants expected to bind KIX more tightly than the wild-type MLL TAD. Mutant peptides with higher helical propensities were selected for experimental characterization. We found that the T2857W mutant of the MLL TAD peptide had the highest binding affinity for KIX compared to the other 12 peptides designed in this study. Moreover, the peptide had a high inhibitory effect on the KIX-MLL interaction with a half-maximal inhibitory concentration close to the dissociation constant for this interaction. To our knowledge, this peptide has the highest affinity for KIX among all previously reported inhibitors that target the MLL site of KIX. Thus, our approach may be useful for rationally developing helical peptides that inhibit protein-protein interactions implicated in the progression of various diseases.

Modular Peptide Probe for Protein Analysis

The analysis and regulation of proteins are of great significance for the development of disease diagnosis and treatment. However, complicated analytical environment and complex protein structure severely limit the accuracy of their analysis results. Nowadays, ascribing to the editability and bioactivity of peptides, peptide-based probes could meet the requirements of good selectivity and high affinity to overcome the challenges. In this review, we summarize the advances in the use of modular peptide probes for proteins analysis. It focuses on how to design and optimize the structure of probes, as well as their performance. Then, the strategies and application to improve the analysis result of modular peptide probes are introduced. Finally, we also discuss current challenge and provide some ideas for the future direction for modular peptide probes, hoping to accelerate their clinical transformation.

Synthesis and Studies of Bulky Cycloalkyl α,β-Dehydroamino Acids that Enhance Proteolytic Stability

Three new bulky cycloalkyl α,β-dehydroamino acids (ΔAAs) have been designed and synthesized. Each residue enhances the rigidity of model peptides and their stability to proteolysis, with larger ring sizes exhibiting greater effects. Peptides containing bulky cycloalkyl ΔAAs are inert to conjugate addition by a nucleophilic thiol. The results suggest that these residues will be effective tools for improving the proteolytic stability of bioactive peptides.

Abiotic Synthetic Antibodies to Target a Specific Protein Domain and Inhibit Its Function

The Bacillus thuringiensis (Bt) Cry proteins are widely used in insect pest control. Despite their economic benefits, remaining concerns over potential ecological and health risks warrant their ongoing surveillance. Affinity reagents, most often antibodies, protein scaffolds, and aptamers, are the traditional tools used for protein binding and detection. We report a synthetic antibody (SA) alternative to traditional biological affinity reagents for binding Bt Cry proteins. Analysis of hotspots of the Bt Cry protein-insect midgut cadherin-like receptor complexes was used for the design of the SA. The SA was selected from a small focused library of hydrogel copolymers containing functional monomers complementary to key exposed hotspots of Bt Cry proteins. A directed chemical evolution identified a SA, APhe-NP23, with affinity and selectivity for Bt Cry1Ab/Ac proteins. The putative intermolecular polymer-protein interfaces were identified by the SA's uptake of Bt Cry1Ac pepsin hydrolysates, binding epitope mutation studies, and protein-protein inhibition studies of the toxin binding to its native insect receptor binding domains. The SA inhibitor binds to the same protein domains as the insect's cadherin-like receptors, Bt-R₁ and SeCad1b. The SA binds rapidly to Bt Cry1Ab/Ac with high capacity, is pH-responsive, and is synthesized reproducibly. We believe that a hotspot-directed approach is general for creation of abiotic protein affinity reagents that target functional protein domains. Affinity ligands are typically high-information content biologicals. Their structure and function are determined from their amino acid or oligo sequence. In contract, the SA described in this work is a statistical copolymer that lacks sequence specificity. These results are an important contribution to the concept that randomness and biospecificity are not mutually exclusive.

Design of Protein Segments and Peptides for Binding to Protein Targets

Recent years have witnessed a rise in methods for accurate prediction of structure and design of novel functional proteins. Design of functional protein fragments and peptides occupy a small, albeit unique, space within the general field of protein design. While the smaller size of these peptides allows for more exhaustive computational methods, flexibility in their structure and sparsity of data compared to proteins, as well as presence of noncanonical building blocks, add additional challenges to their design. This review summarizes the current advances in the design of protein fragments and peptides for binding to targets and discusses the challenges in the field, with an eye toward future directions.

Characterization of Hydrophilic α-Helical Hot Spots on the Protein-Protein Interaction Interfaces for the Design of α-Helix Mimetics

The cooperativity index, K_c, was developed to examine the binding synergy between hot spots of the ligand-protein. For the first time, the convergence of the side-chain spatial arrangements of hydrophilic α-helical hot spots Thr, Tyr, Asp, Asn, Ser, Cys, and His in protein-protein interaction (PPI) complex structures was disclosed and quantified by developing novel clustering models. In-depth analyses revealed the driving force for the protein-protein binding conformation convergence of hydrophilic α-helical hot spots. This observation allows deriving pharmacophore models to design new mimetics for hydrophilic α-helical hot spots. A computational protocol was developed to search amino acid analogues and small-molecule mimetics for each hydrophilic α-helical hot spot. As a pilot study, diverse building blocks of commercially available nonstandard L-type α-amino acids and the phenyl ring-containing small-molecule fragments were obtained, which serve as a fragment collection to mimic hydrophilic α-helical hot spots for the improvement of binding affinity, selectivity, physicochemical properties, and synthesis accessibility of α-helix mimetics.

Peptide Tethering: Pocket-Directed Fragment Screening for Peptidomimetic Inhibitor Discovery

Constrained peptides have proven to be a rich source of ligands for protein surfaces, but are often limited in their binding potency. Deployment of nonnatural side chains that access unoccupied crevices on the receptor surface offers a potential avenue to enhance binding affinity. We recently described a computational approach to create topographic maps of protein surfaces to guide the design of nonnatural side chains [J. Am. Chem. Soc. 2017, 139, 15560]. The computational method, AlphaSpace, was used to predict peptide ligands for the KIX domain of the p300/CBP coactivator. KIX has been the subject of numerous ligand discovery strategies, but potent inhibitors of its interaction with transcription factors remain difficult to access. Although the computational approach provided a significant enhancement in the binding affinity of the peptide, fine-tuning of nonnatural side chains required an experimental screening method. Here we implement a peptide-tethering strategy to screen fragments as nonnatural side chains on conformationally defined peptides. The combined computational-experimental approach offers a general framework for optimizing peptidomimetics as inhibitors of protein-protein interactions.

Optimizing CSP1 Analogs for Modulating Quorum Sensing in Streptococcus pneumoniae with Bulky, Hydrophobic Nonproteogenic Amino Acid Substitutions

The prompt appearance of multiantibiotic-resistant bacteria necessitates finding alternative treatments that can attenuate bacterial infections while minimizing the rate of antibiotic resistance development. Streptococcus pneumoniae, a notorious human pathogen, is responsible for severe antibiotic-resistant infections. Its pathogenicity is influenced by a cell-density communication system, termed quorum sensing (QS). As a result, controlling QS through the development of peptide-based QS modulators may serve to attenuate pneumococcal infections. Herein, we set out to evaluate the impact of the introduction of bulkier, nonproteogenic side-chain residues on the hydrophobic binding face of CSP1 to optimize receptor-binding interactions in both of the S. pneumoniae specificity groups. Our results indicate that these substitutions optimize the peptide–protein binding interactions, yielding several pneumococcal QS modulators with high potency. Moreover, pharmacological evaluation of lead analogs revealed that the incorporation of nonproteogenic amino acids increased the peptides’ half-life towards enzymatic degradation while remaining nontoxic. Overall, our data convey key considerations for SAR using nonproteogenic amino acids, which provide analogs with better pharmacological properties.

The prompt appearance of multiantibiotic-resistant bacteria necessitates finding alternative treatments that can attenuate bacterial infections while minimizing the rate of antibiotic resistance development.

Towards optimizing peptide-based inhibitors of protein-protein interactions: predictive saturation variation scanning (PreSaVS)

A simple-to-implement and experimentally validated computational workflow for sequence modification of peptide inhibitors of protein–protein interactions (PPIs) is described.

An experimentally validated approach for in silico modification of peptide based protein–protein interaction inhibitors is described.

A Dual-Site Inhibitor of CBP/p300 KIX is a Selective and Effective Modulator of Myb

The protein-protein interaction between the KIX motif of the transcriptional coactivator CBP/p300 and the transcriptional activator Myb is a high-value target due to its established role in certain acute myeloid leukemias (AML) and potential contributions to other cancers. However, the CBP/p300 KIX domain has multiple binding sites, several structural homologues, many binding partners, and substantial conformational plasticity, making it challenging to specifically target using small-molecule inhibitors. Here, we report a picomolar dual-site inhibitor (MybLL-tide) of the Myb-CBP/p300 KIX interaction. MybLL-tide has higher affinity for CBP/p300 KIX than any previously reported compounds while also possessing 5600-fold selectivity for the CBP/p300 KIX domain over other coactivator domains. MybLL-tide blocks the association of CBP and p300 with Myb in the context of the proteome, leading to inhibition of key Myb·KIX-dependent genes in AML cells. These results show that MybLL-tide is an effective, modifiable tool to selectively target the KIX domain and assess transcriptional effects in AML cells and potentially other cancers featuring aberrant Myb behavior. Additionally, the dual-site design has applicability to the other challenging coactivators that bear multiple binding surfaces.

Identification of Secondary Binding Sites on Protein Surfaces for Rational Elaboration of Synthetic Protein Mimics

Minimal mimics of protein conformations provide rationally designed ligands to modulate protein function. The advantage of minimal mimics is that they can be chemically synthesized and coaxed to be proteolytically resistant; a key disadvantage is that minimization of the protein binding epitope may be associated with loss of affinity and specificity. Several approaches to overcome this challenge may be envisioned, including deployment of covalent warheads and use of nonnatural residues to improve contacts with the binding surface. Herein, we describe our computational and experimental efforts to enhance the minimal protein mimics with fragments that can contact undiscovered binding pockets on Mdm2 and MdmX-two well-studied protein partners of p53.

A Fungal Defensin Targets the SARS-CoV-2 Spike Receptor-Binding Domain

Coronavirus Disease 2019 (COVID-19) elicited by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is calling for novel targeted drugs. Since the viral entry into host cells depends on specific interactions between the receptor-binding domain (RBD) of the viral Spike protein and the membrane-bound monocarboxypeptidase angiotensin converting enzyme 2 (ACE2), the development of high affinity RBD binders to compete with human ACE2 represents a promising strategy for the design of therapeutics to prevent viral entry. Here, we report the discovery of such a binder and its improvement via a combination of computational and experimental approaches. The binder micasin, a known fungal defensin from the dermatophytic fungus Microsporum canis with antibacterial activity, can dock to the crevice formed by the receptor-binding motif (RBM) of RBD via an extensive shape complementarity interface (855.9 Å2 in area) with numerous hydrophobic and hydrogen-bonding interactions. Using microscale thermophoresis (MST) technique, we confirmed that micasin and its C-terminal γ-core derivative with multiple predicted interacting residues exhibited a low micromolar affinity to RBD. Expanding the interface area of micasin through a single point mutation to 970.5 Å2 accompanying an enhanced hydrogen bond network significantly improved its binding affinity by six-fold. Our work highlights the naturally occurring fungal defensins as an emerging resource that may be suitable for the development into antiviral agents for COVID-19.

Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational "anchor extension" methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC₅₀ values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.

A Sos proteomimetic as a pan-Ras inhibitor

Aberrant Ras signaling is linked to a wide spectrum of hyperproliferative diseases, and components of the signaling pathway, including Ras, have been the subject of intense and ongoing drug discovery efforts. The cellular activity of Ras is modulated by its association with the guanine nucleotide exchange factor Son of sevenless (Sos), and the high-resolution crystal structure of the Ras-Sos complex provides a basis for the rational design of orthosteric Ras ligands. We constructed a synthetic Sos protein mimic that engages the wild-type and oncogenic forms of nucleotide-bound Ras and modulates downstream kinase signaling. The Sos mimic was designed to capture the conformation of the Sos helix-loop-helix motif that makes critical contacts with Ras in its switch region. Chemoproteomic studies illustrate that the proteomimetic engages Ras and other cellular GTPases. The synthetic proteomimetic resists proteolytic degradation and enters cells through macropinocytosis. As such, it is selectively toxic to cancer cells with up-regulated macropinocytosis, including those that feature oncogenic Ras mutations.

Structure, function and inhibition of critical protein-protein interactions involving mixed lineage leukemia 1 and its fusion oncoproteins

Mixed lineage leukemia 1 (MLL1, also known as MLL or KMT2A) is an important transcription factor and histone-H3 lysine-4 (H3K4) methyltransferase. It is a master regulator for transcription of important genes (e.g., Hox genes) for embryonic development and hematopoiesis. However, it is largely dispensable in matured cells. Dysregulation of MLL1 leads to overexpression of certain Hox genes and eventually leukemia initiation. Chromosome translocations involving MLL1 cause ~ 75% of acute leukemia in infants and 5–10% in children and adults with a poor prognosis. Targeted therapeutics against oncogenic fusion MLL1 (onco-MLL1) are therefore needed. Onco-MLL1 consists of the N-terminal DNA-interacting domains of MLL1 fused with one of > 70 fusion partners, among which transcription cofactors AF4, AF9 and its paralog ENL, and ELL are the most frequent. Wild-type (WT)- and onco-MLL1 involve numerous protein–protein interactions (PPI), which play critical roles in regulating gene expression in normal physiology and leukemia. Moreover, WT-MLL1 has been found to be essential for MLL1-rearranged (MLL1-r) leukemia. Rigorous studies of such PPIs have been performed and much progress has been achieved in understanding their structures, structure–function relationships and the mechanisms for activating gene transcription as well as leukemic transformation. Inhibition of several critical PPIs by peptides, peptidomimetic or small-molecule compounds has been explored as a therapeutic approach for MLL1-r leukemia. This review summarizes the biological functions, biochemistry, structure and inhibition of the critical PPIs involving MLL1 and its fusion partner proteins. In addition, challenges and perspectives of drug discovery targeting these PPIs for the treatment of MLL1-r leukemia are discussed.

High-throughput determination of protein affinities using unmodified peptide libraries in nanomolar scale

Protein-protein interactions (PPIs) are of fundamental importance for our understanding of physiology and pathology. PPIs involving short, linear motifs play a major role in immunological recognition, signaling, and regulation and provide attractive starting points for pharmaceutical intervention. Yet, state-of-the-art protein-peptide affinity determination approaches exhibit limited throughput and sensitivity, often resulting from ligand immobilization, labeling, or synthesis. Here, we introduce a high-throughput method for in-solution analysis of protein-peptide interactions using a phenomenon called temperature related intensity change (TRIC). We use TRIC for the identification and fine-mapping of low- and high-affinity protein interaction sites and the definition of sequence binding requirements. Validation is achieved by microarray-based studies using wild-type and mutated recombinant protein and the native protein within tissue lysates. On-chip neutralization and strong correlation with structural data establish TRIC as a quasi-label-free method to determine binding affinities of unmodified peptide libraries with large dynamic range.

Identification of β-strand mediated protein-protein interaction inhibitors using ligand-directed fragment ligation

β-Strand mediated protein-protein interactions (PPIs) represent underexploited targets for chemical probe development despite representing a significant proportion of known and therapeutically relevant PPI targets. β-Strand mimicry is challenging given that both amino acid side-chains and backbone hydrogen-bonds are typically required for molecular recognition, yet these are oriented along perpendicular vectors. This paper describes an alternative approach, using GKAP/SHANK1 PDZ as a model and dynamic ligation screening to identify small-molecule replacements for tranches of peptide sequence. A peptide truncation of GKAP functionalized at the N- and C-termini with acylhydrazone groups was used as an anchor. Reversible acylhydrazone bond exchange with a library of aldehyde fragments in the presence of the protein as template and in situ screening using a fluorescence anisotropy (FA) assay identified peptide hybrid hits with comparable affinity to the GKAP peptide binding sequence. Identified hits were validated using FA, ITC, NMR and X-ray crystallography to confirm selective inhibition of the target PDZ-mediated PPI and mode of binding. These analyses together with molecular dynamics simulations demonstrated the ligands make transient interactions with an unoccupied basic patch through electrostatic interactions, establishing proof-of-concept that this unbiased approach to ligand discovery represents a powerful addition to the armory of tools that can be used to identify PPI modulators.

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.

An in silico mechanistic insight into HDAC8 activation facilitates the discovery of new small-molecule activators

Research interest in the development of histone deacetylase 8 (HDAC8) activators has substantially increased since loss-of-function HDAC8 mutations were found in patients with Cornelia de Lange syndrome (CdLS). A series of N-acetylthioureas (e.g., TM-2-51) have been identified as HDAC8-selective activators, among others; however, their activation mechanisms remain elusive. Herein, we performed molecular dynamics (MD) simulations and fragment-centric topographical mapping (FCTM) to investigate the mechanism of HDAC8 activation. Our results revealed that improper binding of the coumarin group of fluorescent substrates leads to the "flipping out" of catalytic residue Y306, which reduces the enzymatic activity of HDAC8 towards fluorescent substrates. A pocket between the coumarin group of the substrate and thed catalytic residue Y306 was filled with the activator TM-2-51, which not only enhanced binding between HDAC8 and the fluorescent substrate complex but also stabilized Y306 in a catalytically active conformation. Based on this newly proposed substrate-dependent activation mechanism, we performed structure-based virtual screening and successfully identified low-molecular-weight scaffolds as new HDAC8 activators.

Covalent Targeting of Ras G12C by Rationally Designed Peptidomimetics

Protein-protein interactions (PPIs) play a critical role in fundamental biological processes. Competitive inhibition of these interfaces requires compounds that can access discontinuous binding epitopes along a large, shallow binding surface area. Conformationally defined protein surface mimics present a viable route to target these interactions. However, the development of minimal protein mimics that engage intracellular targets with high affinity remains a major challenge because mimicry of a portion of the binding interface is often associated with the loss of critical binding interactions. Covalent targeting provides an attractive approach to overcome the loss of noncovalent contacts but have the inherent risk of dominating noncovalent contacts and increasing the likelihood of nonselective binding. Here, we report the iterative design of a proteolytically stable α3β chimeric helix mimic that covalently targets oncogenic Ras G12C as a model system. We explored several electrophiles to optimize preferential alkylation with the desired C12 on Ras. The designed lead peptide modulates nucleotide exchange, inhibits activation of the Ras-mediated signaling cascade, and is selectively toxic toward mutant Ras G12C cancer cells. The relatively high frequency of acquired cysteines as missense mutations in cancer and other diseases suggests that covalent peptides may offer an untapped therapeutic approach for targeting aberrant protein interactions.

Modulation of virus-induced NF-κB signaling by NEMO coiled coil mimics

Protein-protein interactions featuring intricate binding epitopes remain challenging targets for synthetic inhibitors. Interactions of NEMO, a scaffolding protein central to NF-κB signaling, exemplify this challenge. Various regulators are known to interact with different coiled coil regions of NEMO, but the topological complexity of this protein has limited inhibitor design. We undertook a comprehensive effort to block the interaction between vFLIP, a Kaposi’s sarcoma herpesviral oncoprotein, and NEMO using small molecule screening and rational design. Our efforts reveal that a tertiary protein structure mimic of NEMO is necessary for potent inhibition. The rationally designed mimic engages vFLIP directly causing complex disruption, protein degradation and suppression of NF-κB signaling in primary effusion lymphoma (PEL). NEMO mimic treatment induces cell death and delays tumor growth in a PEL xenograft model. Our studies with this inhibitor reveal the critical nexus of signaling complex stability in the regulation of NF-κB by a viral oncoprotein.

NF-κB signalling involves the scaffold protein NEMO, which can be bound by the oncoprotein vFLIP to promote cell survival and oncogenic transformation. Here the authors rationally engineer a tertiary protein mimic of NEMO to disrupt the vFLIP-NEMO interaction to induce cell death.

Inhibition of striatal-enriched protein tyrosine phosphatase by targeting computationally revealed cryptic pockets

Cryptic pockets, which are not apparent in crystallographic structures, provide promising alternatives to traditional binding sites for drug development. However, identifying cryptic pockets is extremely challenging and the therapeutic potential of cryptic pockets remains unclear. Here, we reported the discovery of novel inhibitors for striatal-enriched protein tyrosine phosphatase (STEP), a potential drug target for multiple neuropsychiatric disorders, based on cryptic pocket detection. By combining the use of molecular dynamics simulations and fragment-centric topographical mapping, we identified transiently open cryptic pockets and identified 12 new STEP inhibition scaffolds through structure-based virtual screening. Site-directed mutagenesis verified the binding of ST3 with the predicted cryptic pockets. Moreover, the most potent and selective inhibitors could modulate the phosphorylation of both ERK1/2 and Pyk2 in PC12 cells.

AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters

Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.

Recognition of PDL1/L2 by different induced-fit mechanisms of PD1: a comparative study of molecular dynamics simulations

The different binding mechanism for PD1/PDL1 and PD1/PDL2 complexes.

Complex Stability is Encoded in Binding Patch Softness: a Monomer-Based Approach to Predict Inter-Subunit Affinity of Protein Dimers

Knowledge about the structure and stability of protein-protein interactions is vital to decipher the behavior of protein systems. Prediction of protein complexes' stability is an interesting topic in the field of structural biology. There are some promising published computational approaches that predict the affinity between subunits of protein dimers using 3D structures of both subunits. In the current study, we classify protein complexes with experimentally measured affinities into distinct classes with different mean affinities. By predicting the mechanical stiffness of the protein binding patch (PBP) region on a single subunit, we successfully predict the assigned affinity class of the PBP in the classification step. Now to predict the experimentally measured affinity between protein monomers in solution, we just need the 3D structure of the suggested PBP on one subunit of the proposed dimer. We designed the SEPAS software and have made the software freely available for academic non-commercial research purposes at " http://biophysics.ir/affinity ". SEPAS predicts the stability of the intended dimer in a classwise manner by utilizing the computed mechanical stiffness of the introduced binding site on one subunit with the minimum accuracy of 0.72.

Engineering Methyllysine Writers and Readers for Allele-Specific Regulation of Protein-Protein Interactions

Protein-protein interactions mediated by methyllysine are ubiquitous in biological systems. Specific perturbation of such interactions has remained a challenging endeavor. Herein, we describe an allele-specific strategy toward an engineered protein-protein interface orthogonal to the human proteome. We develop a methyltransferase (writer) variant that installs aryllysine moiety on histones that can only be recognized by an engineered chromodomain (reader). We establish biochemical integrity of the engineered interface, provide structural evidence for orthogonality and validate its applicability to identify transcriptional regulators. Our approach provides an unprecedented strategy for specific manipulation of the methyllysine interactome.

Design, Synthesis, and Biological Evaluation of 2,4-Imidazolinedione Derivatives as HDAC6 Isoform-Selective Inhibitors

Histone deacetylase 6 (HDAC6) has emerged as a promising drug target for various human diseases, including diverse neurodegenerative diseases and cancer. Herein, we reported a series of 2,4-imidazolinedione derivatives as novel HDAC6 isoform-selective inhibitors based on structure-based drug design. Most target compounds exhibit good profiles in a preliminary screening concerning HDAC6 inhibitory activities. Moreover, the most active compound 10c increases the acetylation level of α-tubulin with little effect on the acetylation of histone H3. Further biological evaluation suggested that potent compound 10c, which possesses good antiproliferative activity, could induce apoptosis in HL-60 cell by activating caspase 3.

Identification and structure-function analyses of an allosteric inhibitor of the tyrosine phosphatase PTPN22

Protein-tyrosine phosphatase nonreceptor type 22 (PTPN22) is a lymphoid-specific tyrosine phosphatase (LYP), and mutations in the PTPN22 gene are highly correlated with a spectrum of autoimmune diseases. However, compounds and mechanisms that specifically inhibit LYP enzymes to address therapeutic needs to manage these diseases remain to be discovered. Here, we conducted a similarity search of a commercial database for PTPN22 inhibitors and identified several LYP inhibitor scaffolds, which helped identify one highly active inhibitor, NC1. Using noncompetitive inhibition curve and phosphatase assays, we determined NC1's inhibition mode toward PTPN22 and its selectivity toward a panel of phosphatases. We found that NC1 is a noncompetitive LYP inhibitor and observed that it exhibits selectivity against other protein phosphatases and effectively inhibits LYP activity in lymphoid T cells and modulates T-cell receptor signaling. Results from site-directed mutagenesis, fragment-centric topographic mapping, and molecular dynamics simulation experiments suggested that NC1, unlike other known LYP inhibitors, concurrently binds to a "WPD" pocket and a second pocket surrounded by an LYP-specific insert, which contributes to its selectivity against other phosphatases. Moreover, using a newly developed method to incorporate the unnatural amino acid 2-fluorine-tyrosine and ¹⁹F NMR spectroscopy, we provide direct evidence that NC1 allosterically regulates LYP activity by restricting WPD-loop movement. In conclusion, our approach has identified a new allosteric binding site in LYP useful for selective LYP inhibitor development; we propose that the ¹⁹F NMR probe developed here may also be useful for characterizing allosteric inhibitors of other tyrosine phosphatases.

A protein interaction free energy model based on amino acid residue contributions: Assessment of point mutation stability of T4 lysozyme

Here we present a model to estimate the interaction free energy contribution of each amino acid residue of a given protein. Protein interaction energy is described in terms of per-residue interaction factors, μ. Multibody interactions are implicitly captured in μ through the combination of amino acid terms (γ) guided by local conformation indices (σ). The model enables construction of an interaction factor heat map for a protein in a given fold, allows prima facie assessment of the degree of residue-residue interaction, and facilitates a qualitative and quantitative evaluation of protein association properties. The model was used to compute thermal stability of T4 bacteriophage lysozyme mutants across seven sites. Qualitative assessment of mutational effects provides a straightforward rationale regarding whether a particular site primarily perturbs native or non-native states, or both. The presented model was found to be in good agreement with experimental mutational data (R 2 = 0.73) and suggests an approach by which to convert structure space into energy space.

Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening: A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors

Accurate protein structure in the ligand-bound state is a prerequisite for successful structure-based virtual screening (SBVS). Therefore, applications of SBVS against targets for which only an apo structure is available may be severely limited. To address this constraint, we developed a computational strategy to explore the ligand-bound state of a target protein, by combined use of molecular dynamics simulation, MM/GBSA binding energy calculation, and fragment-centric topographical mapping. Our computational strategy is validated against low-molecular weight protein tyrosine phosphatase (LMW-PTP) and then successfully employed in the SBVS against protein tyrosine phosphatase receptor type O (PTPRO), a potential therapeutic target for various diseases. The most potent hit compound GP03 showed an IC₅₀ value of 2.89 μM for PTPRO and possessed a certain degree of selectivity toward other protein phosphatases. Importantly, we also found that neglecting the ligand energy penalty upon binding partially accounts for the false positive SBVS hits. The preliminary structure-activity relationships of GP03 analogs are also reported.

The Bump-and-Hole Tactic: Expanding the Scope of Chemical Genetics

Successful mapping of the human genome has sparked a widespread interest in deciphering functional information encoded in gene sequences. However, because of the high degree of conservation in sequences along with topological and biochemical similarities among members of a protein superfamily, uncovering physiological role of a particular protein has been a challenging task. Chemical genetic approaches have made significant contributions toward understanding protein function. One such effort, dubbed the bump-and-hole approach, has convincingly demonstrated that engineering at the protein-small molecule interface constitutes a powerful method for elucidating the function of a specific gene product. By manipulating the steric component of protein-ligand interactions in a complementary manner, an orthogonal system is developed to probe a specific enzyme-cofactor pair without interference from related members. This article outlines current efforts to expand the approach for diverse protein classes and their applications. Potential future innovations to address contemporary biological problems are highlighted as well.

Hydrogen Bond Surrogate Stabilization of β-Hairpins

Peptide secondary and tertiary structure motifs frequently serve as inspiration for the development of protein-protein interaction (PPI) inhibitors. While a wide variety of strategies have been used to stabilize or imitate α-helices, similar strategies for β-sheet stabilization are more limited. Synthetic scaffolds that stabilize reverse turns and cross-strand interactions have provided important insights into β-sheet stability and folding. However, these templates occupy regions of the β-sheet that might impact the β-sheet's ability to bind at a PPI interface. Here, we present the hydrogen bond surrogate (HBS) approach for stabilization of β-hairpin peptides. The HBS linkage replaces a cross-strand hydrogen bond with a covalent linkage, conferring significant conformational and proteolytic resistance. Importantly, this approach introduces the stabilizing linkage in the buried β-sheet interior, retains all side chains for further functionalization, and allows efficient solid-phase macrocyclization. We anticipate that HBS stabilization of PPI β-sheets will enhance the development of β-sheet PPI inhibitors and expand the repertoire of druggable PPIs.

Defining the hydrophobic interactions that drive competence stimulating peptide (CSP)-ComD binding in Streptococcus pneumoniae

Quorum sensing (QS) is a cell–cell communication mechanism that enables bacteria to assess their population density and alter their behavior upon reaching high cell number. Many bacterial pathogens utilize QS to initiate an attack on their host, thus QS has attracted significant attention as a potential antivirulence alternative to traditional antibiotics. Streptococcus pneumoniae, a notorious human pathogen responsible for a variety of acute and chronic infections, utilizes the competence regulon and its associated signaling peptide, the competence stimulating peptide (CSP), to acquire antibiotic resistance and establish an infection. In this work, we sought to define the binding pockets within the ComD1 receptor used for binding the hydrophobic side-chain residues in CSP1 through the introduction of highly-conservative point mutations within the peptide. Optimization of these binding interactions could lead to the development of highly potent CSP-based QS modulators while the inclusion of non-natural amino acids within the CSP sequence would confer resistance to protease degradation, a requirement for drug candidates.

Engineered protein scaffolds as leads for synthetic inhibitors of protein-protein interactions

Rationally designed protein-protein interaction inhibitors mimic interfacial binding epitopes, specifically residues that contribute significantly to binding. However, direct mimicry often does not lead to high affinity ligands because the natural complexes themselves are functionally transient and of low affinity. The mimics typically need to be optimized for potency. Engineered proteins displaying conformationally-defined epitopes may serve as attractive alternatives to natural protein partners as they can be strictly screened for tight binding. The advantage of focused screens with conformationally-defined protein scaffolds is that conservation of the geometry of the natural binding epitopes may preserve binding site specificity while allowing direct mimicry by various synthetic secondary structure scaffolds. Here we review different classes of engineered proteins for their binding epitope geometry and as leads for synthetic secondary and tertiary structure mimics.