HotRegion: a database of predicted hot spot clusters

PPInterface: A Comprehensive Dataset of 3D Protein-Protein Interface Structures

The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein-protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein-protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset's utility and accessibility. The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/.

Deciphering ACE2-RBD binding affinity through peptide scanning: A molecular dynamics simulation approach

Rapid discovery of target information for protein-protein interactions (PPIs) is significant in drug design, diagnostics, vaccine development, antibody therapy, etc. Peptide microarray is an ideal tool for revealing epitope information of PPIs. In this work, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike receptor-binding domain (RBD) and the host cell receptor angiotensin-converting enzyme 2 (ACE2) were introduced as a model to study the epitope information of RBD-specific binding to ACE2 via a combination of theoretical calculations and experimental validation. Through dock and molecular dynamics simulations, it was found that among the 22 peptide fragments that consist of RBD, #14 (YNYLYRLFRKSNLKP) has the highest binding strength. Subsequently, the experiments of peptide microarray constructed based on plasmonic materials chip also confirmed the theoretical calculation data. Compared to other methods, such as phage display technology and surface plasmon resonance (SPR), this method is rapid and cost-effective, providing insights into the investigation of pathogen invasion processes and the timely development of peptide drugs and other fields.

Computational approaches for identifying disease-causing mutations in proteins

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

Protein binding sites for drug design

Drug development is a lengthy and challenging process that can be accelerated at early stages by new mathematical approaches and modern computers. To address this important issue, we are developing new mathematical solutions for the detection and characterization of protein binding sites that are important for new drug development. In this review, we present algorithms based on graph theory combined with molecular dynamics simulations that we have developed for studying biological target proteins to provide important data for optimizing the early stages of new drug development. A particular focus is the development of new protein binding site prediction algorithms (ProBiS) and new web tools for modeling pharmaceutically interesting molecules-ProBiS Tools (algorithm, database, web server), which have evolved into a full-fledged graphical tool for studying proteins in the proteome. ProBiS differs from other structural algorithms in that it can align proteins with different folds without prior knowledge of the binding sites. It allows detection of similar binding sites and can predict molecular ligands of various types of pharmaceutical interest that could be advanced to drugs to treat a disease, based on the entire Protein Data Bank (PDB) and AlphaFold database, including proteins not yet in the PDB. All ProBiS Tools are freely available to the academic community at http://insilab.org and https://probis.nih.gov.

Computational Modeling of TP63-TP53 Interaction and Rational Design of Inhibitors: Implications for Therapeutics

Tumor protein p63 (TP63) is a member of the TP53 protein family that are important for development and in tumor suppression. Unlike TP53, TP63 is rarely mutated in cancer, but instead different TP63 isoforms regulate its activity. TA isoforms (TAp63) act as tumor suppressors, whereas ΔN isoforms are strong drivers of squamous or squamous-like cancers. Many of these tumors become addicted to ΔN isoforms and removal of ΔN isoforms result in cancer cell death. Furthermore, some TP53 conformational mutants (TP53CM) gain the ability to interact with TAp63 isoforms and inhibit their antitumorigenic function, while indirectly promoting tumorigenic function of ΔN isoforms, but the exact mechanism of TP63-TP53CM interaction is unclear. The changes in the balance of TP63 isoform activity are crucial to understanding the transition between normal and tumor cells. Here, we modeled TP63-TP53CM complex using computational approaches. We then used our models to design peptides to disrupt the TP63-TP53CM interaction and restore antitumorigenic TAp63 function. In addition, we studied ΔN isoform oligomerization and designed peptides to inhibit its oligomerization and reduce their tumorigenic activity. We show that some of our peptides promoted cell death in a TP63 highly expressed cancer cell line, but not in a TP63 lowly expressed cancer cell line. Furthermore, we performed kinetic-binding assays to validate binding of our peptides to their targets. Our computational and experimental analyses present a detailed model for the TP63-TP53CM interaction and provide a framework for potential therapeutic peptides for the elimination of TP53CM cancer cells.

Protein-protein interaction of RdRp with its co-factor NSP8 and NSP7 to decipher the interface hotspot residues for drug targeting: A comparison between SARS-CoV-2 and SARS-CoV

In this study we explored the molecular mechanism of RdRp (Non-Structural Protein, NSP12) interaction with its co-factors NSP7 and NSP8 which is the main toolbox for RNA replication and transcription of SARS-CoV-2 and SARS-CoV. The replication complex is a heterotetramer consists of one NSP12, one NSP7 and two NSP8. Extensive molecular dynamics (MD) simulations were applied on both the heterotetramer complexes to generate the conformations and were used to estimate the MMPBSA binding free energy (BFE) and per-residue energy decomposition of NSP12-NSP8 and NSP12-NSP7 and NSP7-NSP8 complexes. The BFE of SARS-CoV-2 heterotetramer complex with its corresponding partner protein was significantly higher as compared to SARS-CoV. Interface hotspot residues were predicted using different methods implemented in KFC (Knowledge-based FADA and Contracts), HotRegion and Robetta web servers. Per-residue energy decomposition analysis showed that the predicted interface hotspot residues contribute more energy towards the formation of complexes and most of the predicted hotspot residues are clustered together. However, there is a slight difference in the residue-wise energy contribution in the interface NSPs on heterotetramer viral replication complex of both coronaviruses. While the overall replication complex of SARS-CoV-2 was found to be slightly flexible as compared to SARS-CoV. This difference in terms of structural flexibility/stability and energetic characteristics of interface residues including hotspots at PPI interface in the viral replication complexes may be the reason of higher rate of RNA replication of SARS-CoV-2 as compared to SARS-CoV. Overall, the interaction profile at PPI interface such as, interface area, hotspot residues, nature of bonds and energies between NSPs, may provide valuable insights in designing of small molecules or peptide/peptidomimetic ligands which can fit into the PPI interface to disrupt the interaction.

In-silico design of peptides for inhibition of HLA-A*03-KLIETYFSK complex as a new drug design for treatment of multiples sclerosis disease

Multiple sclerosis is recognized as a chronic inflammatory disease. Human leukocyte antigen (HLA) plays an important role in initiating adaptive immune responses. HLA class I is present in almost all nucleated cells and presents the cleaved endogenous peptide antigens to cytotoxic T cells. HLA-A*03 is one of the HLA class I alleles, which is reported as substantially related HLA to MS disease. In 2011, the structure of the HLA-A*03 in complex was identified with an immunodominant proteolipid protein (PLP) epitope (KLIETYFSK). This complex has been reported as an important autoantigen-presenting complex in MS pathogenesis. In this study, new peptides were designed to bind to this complex that may prevent specific pathogenic cytotoxic T cell binding to this autoantigen-presenting complex and CNS demyelination. Herein, 14 new helical peptides containing 19 amino acids were designed and their structures were predicted using the PEP-FOLD server. The binding of each designed peptide to the mentioned complex was then performed. A mutation approach was used by the BeAtMuSiC server to improve the binding affinity of the designed peptide. In each position, amino acid substitutions leading to an increase in the binding affinity of the peptide to the mentioned complex were determined. Finally, the resulting complexes were simulated for 40 ns using AMBER18 software. The results revealed that out of 14 designed peptides, "WRYWWKDWAKQFRQFYRWF" peptide exhibited the highest affinity for binding to the mentioned complex. This peptide can be considered as a potential drug to control multiple sclerosis disease in patients carrying the HLA-A*03 allele.

Gamma Carbonic Anhydrases from Hydrothermal Vent Bacteria: Cases of Alternating Active Site Due to a Long Loop with Proton Shuttle Residue

Accelerated CO2 sequestration uses carbonic anhydrases (CAs) as catalysts; thus, there is much research on these enzymes. The γ-CA from Escherichia coli (EcoCA-γ) was the first γ-CA to display an active site that switches between “open” and “closed” states through Zn2+ coordination by the proton-shuttling His residue. Here, we explored this occurrence in γ-CAs from hydrothermal vent bacteria and also the γ-CA from Methanosarcina thermophila (Cam) using molecular dynamics. Ten sequences were analyzed through multiple sequence alignment and motif analysis, along with three others from a previous study. Conservation of residues and motifs was high, and phylogeny indicated a close relationship amongst the sequences. All structures, like EcoCA-γ, had a long loop harboring the proton-shuttling residue. Trimeric structures were modeled and simulated for 100 ns at 423 K, with all the structures displaying thermostability. A shift between “open” and “closed” active sites was observed in the 10 models simulated through monitoring the behavior of the His proton-shuttling residue. Cam, which has two Glu proton shuttling residues on long loops (Glu62 and Glu84), also showed an active site switch affected by the first Glu proton shuttle, Glu62. This switch was thus concluded to be common amongst γ-CAs and not an isolated occurrence.

Interactome analysis of Bag-1 isoforms reveals novel interaction partners in endoplasmic reticulum-associated degradation

Bag-1 is a multifunctional protein that regulates Hsp70 chaperone activity, apoptosis, and proliferation. The three major Bag-1 isoforms have different subcellular localizations and partly non-overlapping functions. To identify the detailed interaction network of each isoform, we utilized mass spectrometry-based proteomics and found that interactomes of Bag-1 isoforms contained many common proteins, with variations in their abundances. Bag-1 interactomes were enriched with proteins involved in protein processing and degradation pathways. Novel interaction partners included VCP/p97; a transitional ER ATPase, Rad23B; a shuttling factor for ubiquitinated proteins, proteasome components, and ER-resident proteins, suggesting a role for Bag-1 also in ER-associated protein degradation (ERAD). Bag-1 pull-down from cells and tissues from breast cancer patients validated these interactions and showed cancer-related prominence. Using in silico predictions we detected hotspot residues of Bag-1. Mutations of these residues caused loss of binding to protein quality control elements and impaired proteasomal activity in MCF-7 cells. Following CD147 glycosylation pattern, we showed that Bag-1 downregulated VCP/p97-dependent ERAD. Overall, our data extends the interaction map of Bag-1, and broadens its role in protein homeostasis. Targeting the interaction surfaces revealed in this study might be an effective strategy in the treatment of cancer.

mPPI: a database extension to visualize structural interactome in a one-to-many manner

Protein-protein interaction (PPI) databases with structural information are useful to investigate biological functions at both systematic and atomic levels. However, most existing PPI databases only curate binary interactome. From the perspective of the display and function of PPI, as well as the structural binding interface, the related database and resources are summarized. We developed a database extension, named mPPI, for PPI structural visualization. Comparing with the existing structural interactomes that curate resolved PPI conformation in pairs, mPPI can visualize target protein and its multiple interactors simultaneously, which facilitates multi-target drug discovery and structure prediction of protein macro-complexes. By employing a protein-protein docking algorithm, mPPI largely extends the coverage of structural interactome from experimentally resolved complexes. mPPI is designed to be a customizable and convenient plugin for PPI databases. It possesses wide potential applications for various PPI databases, and it has been used for a neurodegenerative disease-related PPI database as demonstration. Scripts and implementation guidelines of mPPI are documented at the database tool website. Database URL  http://bis.zju.edu.cn/mppi/.

The ribosomal protein eL21 interacts with the protein lysine methyltransferase SMYD2 and regulates its steady state levels

The protein lysine methyltransferase, SMYD2 is involved in diverse cellular events by regulating protein functions through lysine methylation. Though several substrate proteins of SMYD2 are well-studied, only a limited number of its interaction partners have been identified and characterized. Here, we performed a yeast two-hybrid screening of SMYD2 and found that the ribosomal protein, eL21 could interact with SMYD2. SMYD2-eL21 interaction in the human cells was confirmed by immunoprecipitation methods. In vitro pull-down assays revealed that SMYD2 interacts with eL21 directly through its SET and MYND domain. Computational mapping, followed by experimental studies identified that Lys81 and Lys83 residues of eL21 are important for the SMYD2-eL21 interaction. Evolutionary analysis showed that these residues might have co-evolved with the emergence of SMYD2. We found that eL21 regulates the steady state levels of SMYD2 by promoting its transcription and inhibiting its proteasomal degradation. Importantly, SMYD2-eL21 interaction plays an important role in regulating cell proliferation and its dysregulation might lead to tumorigenesis. Our findings highlight a novel extra-ribosomal function of eL21 on regulating SMYD2 levels and imply that ribosomal proteins might regulate wide range of cellular functions through protein-protein interactions in addition to their core function in translation.

Normal Mode Analysis of KRas4B Reveals Partner Specific Dynamics

Ras GTPase interacts with its regulators and downstream effectors for its critical function in cellular signaling. Targeting the disrupted mechanisms in Ras-related human cancers requires understanding the distinct dynamics of these protein-protein interactions. We performed normal mode analysis (NMA) of KRas4B in wild-type or mutant monomeric and neurofibromin-1 (NF1), Son of Sevenless 1 (SOS1) or Raf-1 bound dimeric conformational states to reveal partner-specific dynamics of the protein. Gaussian network model (GNM) analysis showed that the known KRas4B lobes further partition into subdomains upon binding to its partners. Furthermore, KRas4B interactions with different partners suppress the flexibility in not only their binding sites but also distant residues in the allosteric lobe in a partner-specific way. The conformational changes can be driven by intrinsic residue fluctuations of the open state KRas4B-GDP, as we illustrated with anisotropic network model (ANM) analysis. The allosteric paths connecting the nucleotide binding residues to the allosteric site at α3-L7 portray differences in the inactive and active states. These findings help in understanding the partner-specific KRas4B dynamics, which could be utilized for therapeutic targeting.

Direct Keap1-kelch inhibitors as potential drug candidates for oxidative stress-orchestrated diseases: A review on In silico perspective

The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.

Neuropsychiatric Symptoms of COVID-19 Explained by SARS-CoV-2 Proteins' Mimicry of Human Protein Interactions

The first clinical symptoms focused on the presentation of coronavirus disease 2019 (COVID-19) have been respiratory failure, however, accumulating evidence also points to its presentation with neuropsychiatric symptoms, the exact mechanisms of which are not well known. By using a computational methodology, we aimed to explain the molecular paths of COVID-19 associated neuropsychiatric symptoms, based on the mimicry of the human protein interactions with SARS-CoV-2 proteins. Methods: Available 11 of the 29 SARS-CoV-2 proteins' structures have been extracted from Protein Data Bank. HMI-PRED (Host-Microbe Interaction PREDiction), a recently developed web server for structural PREDiction of protein-protein interactions (PPIs) between host and any microbial species, was used to find the "interface mimicry" through which the microbial proteins hijack host binding surfaces. Classification of the found interactions was conducted using the PANTHER Classification System. Results: Predicted Human-SARS-CoV-2 protein interactions have been extensively compared with the literature. Based on the analysis of the molecular functions, cellular localizations and pathways related to human proteins, SARS-CoV-2 proteins are found to possibly interact with human proteins linked to synaptic vesicle trafficking, endocytosis, axonal transport, neurotransmission, growth factors, mitochondrial and blood-brain barrier elements, in addition to its peripheral interactions with proteins linked to thrombosis, inflammation and metabolic control. Conclusion: SARS-CoV-2-human protein interactions may lead to the development of delirium, psychosis, seizures, encephalitis, stroke, sensory impairments, peripheral nerve diseases, and autoimmune disorders. Our findings are also supported by the previous in vivo and in vitro studies from other viruses. Further in vivo and in vitro studies using the proteins that are pointed here, could pave new targets both for avoiding and reversing neuropsychiatric presentations.

In Silico Investigation of Potential Applications of Gamma Carbonic Anhydrases as Catalysts of CO2 Biomineralization Processes: A Visit to the Thermophilic Bacteria Persephonella hydrogeniphila, Persephonella marina, Thermosulfidibacter takaii, and Thermus thermophilus

Carbonic anhydrases (CAs) have been identified as ideal catalysts for CO₂ sequestration. Here, we report the sequence and structural analyses as well as the molecular dynamics (MD) simulations of four γ-CAs from thermophilic bacteria. Three of these, Persephonella marina, Persephonella hydrogeniphila, and Thermosulfidibacter takaii originate from hydrothermal vents and one, Thermus thermophilus HB8, from hot springs. Protein sequences were retrieved and aligned with previously characterized γ-CAs, revealing differences in the catalytic pocket residues. Further analysis of the structures following homology modeling revealed a hydrophobic patch in the catalytic pocket, presumed important for CO₂ binding. Monitoring of proton shuttling residue His69 (P. marina γ-CA numbering) during MD simulations of P. hydrogeniphila and P. marina’s γ-CAs (γ-PhCA and γ-PmCA), showed a different behavior to that observed in the γ-CA of Escherichia coli, which periodically coordinates Zn²⁺. This work also involved the search for hotspot residues that contribute to interface stability. Some of these residues were further identified as key in protein communication via betweenness centrality metric of dynamic residue network analysis. T. takaii’s γ-CA showed marginally lower thermostability compared to the other three γ-CA proteins with an increase in conformations visited at high temperatures being observed. Hydrogen bond analysis revealed important interactions, some unique and others common in all γ-CAs, which contribute to interface formation and thermostability. The seemingly thermostable γ-CA from T. thermophilus strangely showed increased unsynchronized residue motions at 423 K. γ-PhCA and γ-PmCA were, however, preliminarily considered suitable as prospective thermostable CO₂ sequestration agents.

In-silico Design of Multi-epitope Vaccine against Nipah Virus using Immunoinformatics Approach

Nipah virus is a pleomorphic virus that causes high mortality with unpredictable outbreaks. The virus also shows high zoonotic potential with long term neurological damage after recovery further adding to the disease burden. An in-silico epitope-based vaccine offers a promising solution to supplement wider efforts to control the viral spread. This is achieved through immunoinformatics approach using a plethora of servers available. We derived cytotoxic T-cell, T-Helper, B-cell and IFN-γ targeting epitopes from surface glycoprotein G. Cytotoxic T-cell specific epitopes, HLA-B*4402, chimeric multiepitope vaccine structures were prepared using homology modelling method. The structures were validated using various methods and docking simulation was performed between epitopes and HLA-B*4402. Similarly, the vaccine construct was docked to Toll like receptor-4 and a molecular dynamics simulation was performed to assess stability of interaction. Both the docking simulations showed stable interactions with their respective receptors. Immune-simulation was carried out to validate the efficacy of vaccine candidate which showed elevated levels of antibodies such as IgM and IgG due to increase in active B cell population. Both in-vitro and in-vivo serological analysis is required for confirmation of vaccine potency. To facilitate this effort, codon optimization was undertaken to remove existing codon bias. The optimized gene sequence was cloned into the PUC19 vector to express in Escherichia coli K12 strain. Additionally, a poly histidine (6xHis) tag was added at the C-terminal end to ease the purification step. The immune-informatics approach hopes to accelerate vaccine development process to reduce the risk of attenuation while increasing the success rates of pre-clinical trials.

Computationally-obtained structural insights into the molecular interactions between Pidilizumab and binding partners DLL1 and PD-1

Pidilizumab is a monoclonal antibody tested against several types of malignancies, such as lymphoma and metastatic melanoma, showing promising results. In 2016, the FDA put Pidilizumab's clinical studies on partial hold due to emerging evidence pointing to the antibody target uncertainty. Although initial studies indicated an interaction with the PD-1 checkpoint receptor, recent updates assert that Pidilizumab binds primarily to Notch ligand DLL1. However, a detailed description of which interactions coordinate antibody-antigen complex formation is lacking. Therefore, this study uses computational tools to identify molecular interactions between Pidilizumab and its reported targets PD-1 and DLL1. A docking methodology was validated and applied to determine the binding modes between modeled Pidilizumab scFvs and the two antigens. We used Molecular Dynamics (MD) simulations to verify the complexes' stability and submitted the resulting trajectory files to MM/PBSA and Principal Component Analysis. A set of different prediction tools determined scFv interface hot-spots. Whereas docking and MD simulations revealed that the antibody fragments do not interact straightforwardly with PD-1, ten scFv hot-spots, including Met93 and Leu112, mediated the interaction with the DLL1 C2 domain. The interaction triggered a conformational selection-like effect on DLL1, allowing new hydrogen bonds on the β3-β4 interface loop. The unprecedented structural data on Pidilizumab's interactions provided novel evidence that its legitimate target is the DLL1 protein and offered structural insight on how these molecules interact, shedding light on the pathways that could be affected by the use of this essential immunobiological.Communicated by Ramaswamy H. Sarma.

Design of novel peptide inhibitors against the conserved bacterial transcription terminator, Rho

The transcription terminator Rho regulates many physiological processes in bacteria, such as antibiotic sensitivity, DNA repair, RNA remodeling, and so forth, and hence, is a potential antimicrobial target, which is unexplored. The bacteriophage P4 capsid protein, Psu, moonlights as a natural Rho antagonist. Here, we report the design of novel peptides based on the C-terminal region of Psu using phenotypic screening methods. The resultant 38-mer peptides, in addition to containing mutagenized Psu sequences, also contained plasmid sequences, fused to their C termini. Expression of these peptides inhibited the growth of Escherichia coli and specifically inhibited Rho-dependent termination in vivo. Peptides 16 and 33 exhibited the best Rho-inhibitory properties in vivo. Direct high-affinity binding of these two peptides to Rho also inhibited the latter's RNA-dependent ATPase and transcription termination functions in vitro. These two peptides remained functional even if eight to ten amino acids were deleted from their C termini. In silico modeling and genetic and biochemical evidence revealed that these two peptides bind to the primary RNA-binding site of the Rho hexamer near its subunit interfaces. In addition, the gene expression profiles of these peptides and Psu overlapped significantly. These peptides also inhibited the growth of Mycobacteria and inhibited the activities of Rho proteins from Mycobacterium tuberculosis, Xanthomonas, Vibrio cholerae, and Salmonella enterica. Our results showed that these novel anti-Rho peptides mimic the Rho-inhibition function of the ∼42-kDa dimeric bacteriophage P4 capsid protein, Psu. We conclude that these peptides and their C-terminal deletion derivatives could provide a basis on which to design novel antimicrobial peptides.

Molecular analysis of a fibrin-degrading enzyme from Bacillus subtilis K2 isolated from the Indonesian soybean-based fermented food moromi

The screening of proteolytic and fibrinolytic bacteria from moromi (an Indonesian soybean-based fermented food) yielded a number of isolates. Based on morphological and biochemical analyses and sequencing of the 16S rRNA gene, the isolate that exhibited the highest proteolytic and fibrinolytic activity was identified as Bacillus subtilis K2. The study was performed to analyze molecular characteristic of a fibrin-degrading enzyme from B. subtilis K2. BLASTn analysis of the nucleotide sequence encoding this fibrinolytic protein demonstrated 73.6% homology with the gene encoding the fibrin-degrading enzyme nattokinase of the B. subtilis subsp. natto, which was isolated from fermented soybean in Japan. An analysis of the putative amino-acid sequence of this protein indicated that it is a serine protease enzyme with aspartate, histidine, and serine in the catalytic triad. This enzyme was determined to be a 26-kDa molecule, as confirmed with a zymogram assay. Further bioinformatic analysis using Protparam demonstrated that the enzyme has a pI of 6.02, low instability index, high aliphatic index, and low GRAVY value. Molecular docking analysis using HADDOCK indicated that there are favorable interactions between subtilisin K2 and the fibrin substrate, as demonstrated by a high binding affinity (ΔG: - 19.4 kcal/mol) and low Kd value (6.3E-15 M). Overall, the study concluded that subtilisin K2 belong to serine protease enzyme has strong interactions with its fibrin substrate and fibrin can be rapidly degraded by this enzyme, suggesting its application as a treatment for thrombus diseases.

Alpha-Carbonic Anhydrases from Hydrothermal Vent Sources as Potential Carbon Dioxide Sequestration Agents: In Silico Sequence, Structure and Dynamics Analyses

With the increase in CO2 emissions worldwide and its dire effects, there is a need to reduce CO2 concentrations in the atmosphere. Alpha-carbonic anhydrases (α-CAs) have been identified as suitable sequestration agents. This study reports the sequence and structural analysis of 15 α-CAs from bacteria, originating from hydrothermal vent systems. Structural analysis of the multimers enabled the identification of hotspot and interface residues. Molecular dynamics simulations of the homo-multimers were performed at 300 K, 363 K, 393 K and 423 K to unearth potentially thermostable α-CAs. Average betweenness centrality (BC) calculations confirmed the relevance of some hotspot and interface residues. The key residues responsible for dimer thermostability were identified by comparing fluctuating interfaces with stable ones, and were part of conserved motifs. Crucial long-lived hydrogen bond networks were observed around residues with high BC values. Dynamic cross correlation fortified the relevance of oligomerization of these proteins, thus the importance of simulating them in their multimeric forms. A consensus of the simulation analyses used in this study suggested high thermostability for the α-CA from Nitratiruptor tergarcus. Overall, our novel findings enhance the potential of biotechnology applications through the discovery of alternative thermostable CO2 sequestration agents and their potential protein design.

Chemometric Models of Differential Amino Acids at the Navα and Navβ Interface of Mammalian Sodium Channel Isoforms

(1) Background: voltage-gated sodium channels (Na_vs) are integral membrane proteins that allow the sodium ion flux into the excitable cells and initiate the action potential. They comprise an α (Na_vα) subunit that forms the channel pore and are coupled to one or more auxiliary β (Na_vβ) subunits that modulate the gating to a variable extent. (2) Methods: after performing homology in silico modeling for all nine isoforms (Na_v1.1α to Na_v1.9α), the Na_vα and Na_vβ protein-protein interaction (PPI) was analyzed chemometrically based on the primary and secondary structures as well as topological or spatial mapping. (3) Results: our findings reveal a unique isoform-specific correspondence between certain segments of the extracellular loops of the Na_vα subunits. Precisely, loop S5 in domain I forms part of the PPI and assists Na_vβ1 or Na_vβ3 on all nine mammalian isoforms. The implied molecular movements resemble macroscopic springs, all of which explains published voltage sensor effects on sodium channel fast inactivation in gating. (4) Conclusions: currently, the specific functions exerted by the Na_vβ1 or Na_vβ3 subunits on the modulation of Na_vα gating remain unknown. Our work determined functional interaction in the extracellular domains on theoretical grounds and we propose a schematic model of the gating mechanism of fast channel sodium current inactivation by educated guessing.

HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry

Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For shedding light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins they do this. Host-Microbe Interaction PREDictor (HMI-PRED) is a user-friendly web server for structural prediction of protein-protein interactions (PPIs) between the host and a microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on "interface mimicry" through which the microbial proteins hijack host binding surfaces. Given the structure of a microbial protein of interest, HMI-PRED will return structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted, and tissue expression of the microbe-targeted host proteins. The server also allows users to upload homology models of microbial proteins. Broadly, it aims at large-scale, efficient identification of HMIs. The prediction results are stored in a repository for community access. HMI-PRED is free and available at https://interactome.ku.edu.tr/hmi.

The In Silico Prediction of Hotspot Residues that Contribute to the Structural Stability of Subunit Interfaces of a Picornavirus Capsid

The assembly of picornavirus capsids proceeds through the stepwise oligomerization of capsid protein subunits and depends on interactions between critical residues known as hotspots. Few studies have described the identification of hotspot residues at the protein subunit interfaces of the picornavirus capsid, some of which could represent novel drug targets. Using a combination of accessible web servers for hotspot prediction, we performed a comprehensive bioinformatic analysis of the hotspot residues at the intraprotomer, interprotomer and interpentamer interfaces of the Theiler’s murine encephalomyelitis virus (TMEV) capsid. Significantly, many of the predicted hotspot residues were found to be conserved in representative viruses from different genera, suggesting that the molecular determinants of capsid assembly are conserved across the family. The analysis presented here can be applied to any icosahedral structure and provides a platform for in vitro mutagenesis studies to further investigate the significance of these hotspots in critical stages of the virus life cycle with a view to identify potential targets for antiviral drug design.

Oncogenic K-Ras4B Dimerization Enhances Downstream Mitogen-activated Protein Kinase Signaling

Ras recruits and activates effectors that transmit receptor-initiated signals. Monomeric Ras can bind Raf; however, Raf's activation requires dimerization, which can be facilitated by Ras dimerization. Previously, we showed that active K-Ras4B dimerizes in silico and in vitro through two major interfaces: (i) β-interface, mapped to Switch I and effector-binding regions, (ii) α-interface at the allosteric lobe. Here, we chose constitutively active K-Ras4B as our control and two double mutants (K101D and R102E; and R41E and K42D) in the α- and β-interfaces. Two of the mutations are from The Cancer Genome Atlas (TCGA) and the Catalogue Of Somatic Mutations In Cancer (COSMIC) data sets. R41 and R102 are found in several adenocarcinomas in Ras isoforms. We performed site-directed mutagenesis, cellular localization experiments, and molecular dynamics (MD) simulations to assess the impact of the mutations on K-Ras4B dimerization and function. α-interface K101D/R102E double mutations reduced dimerization but only slightly reduced downstream phosphorylated extracellular signal-regulated kinase (ERK) (pERK) levels. While β-interface R41E/K42D double mutations did not interfere with dimerization, they almost completely blocked K-Ras4B-mediated ERK phosphorylation. Both double mutations increased downstream phosphorylated Akt (pAkt) levels in cells. Changes in pERK and pAkt levels altered ERK- and Akt-regulated gene expressions, such as EGR1, JUN, and BCL2L11. These results underscore the role of the α-interface in K-Ras4B homodimerization and the β-surface in effector binding. MD simulations highlight that the membrane and hypervariable region (HVR) interact with both α- and β-interfaces of K-Ras4B mutants, respectively, inhibiting homodimerization and probably effector binding. Mutations at both interfaces interfered with mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase signaling but in different forms and extents. We conclude that dimerization is not necessary but enhances downstream MAPK signaling.

Beyond the heterodimer model for mineralocorticoid and glucocorticoid receptor interactions in nuclei and at DNA

Glucocorticoid (GR) and mineralocorticoid receptors (MR) are believed to classically bind DNA as homodimers or MR-GR heterodimers to influence gene regulation in response to pulsatile basal or stress-evoked glucocorticoid secretion. Pulsed corticosterone presentation reveals MR and GR co-occupy DNA only at the peaks of glucocorticoid oscillations, allowing interaction. GR DNA occupancy was pulsatile, while MR DNA occupancy was prolonged through the inter-pulse interval. In mouse mammary 3617 cells MR-GR interacted in the nucleus and at a chromatin-associated DNA binding site. Interactions occurred irrespective of ligand type and receptors formed complexes of higher order than heterodimers. We also detected MR-GR interactions ex-vivo in rat hippocampus. An expanded range of MR-GR interactions predicts structural allostery allowing a variety of transcriptional outcomes and is applicable to the multiple tissue types that co-express both receptors in the same cells whether activated by the same or different hormones.

Analysis of single amino acid variations in singlet hot spots of protein-protein interfaces

Motivation

Single amino acid variations (SAVs) in protein-protein interaction (PPI) sites play critical roles in diseases. PPI sites (interfaces) have a small subset of residues called hot spots that contribute significantly to the binding energy, and they may form clusters called hot regions. Singlet hot spots are the single amino acid hot spots outside of the hot regions. The distribution of SAVs on the interface residues may be related to their disease association.

Results

We performed statistical and structural analyses of SAVs with literature curated experimental thermodynamics data, and demonstrated that SAVs which destabilize PPIs are more likely to be found in singlet hot spots rather than hot regions and energetically less important interface residues. In contrast, non-hot spot residues are significantly enriched in neutral SAVs, which do not affect PPI stability. Surprisingly, we observed that singlet hot spots tend to be enriched in disease-causing SAVs, while benign SAVs significantly occur in non-hot spot residues. Our work demonstrates that SAVs in singlet hot spot residues have significant effect on protein stability and function.

Availability and implementation

The dataset used in this paper is available as Supplementary Material. The data can be found at http://prism.ccbb.ku.edu.tr/data/sav/ as well.

Supplementary information

Supplementary data are available at Bioinformatics online.

Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing

Drug repurposing is a creative and resourceful approach to increase the number of therapies by exploiting available and approved drugs. However, identifying new protein targets for previously approved drugs is challenging. Although new strategies have been developed for drug repurposing, there is broad agreement that there is room for further improvements. In this chapter, we review protein-protein interaction (PPI) interface-targeting strategies for drug repurposing applications. We discuss certain features, such as hot spot residue and hot region prediction and their importance in drug repurposing, and illustrate common methods used in PPI networks to identify drug off-targets. We also collect available online resources for hot spot prediction, binding pocket identification, and interface clustering which are effective resources in polypharmacology. Finally, we provide case studies showing the significance of protein interfaces and hot spots in drug repurposing.

Computational analysis of hot spots and binding mechanism in the PD-1/PD-L1 interaction

Programmed cell death protein-1 (PD-1) is an important immunological checkpoint and plays a vital role in maintaining the peripheral tolerance of the human body by interacting with its ligand PD-L1. The overexpression of PD-L1 in tumor cells induces local immune suppression and helps the tumor cells to evade the endogenous anti-tumor immunity. Developing monoclonal antibodies against the PD-1/PD-L1 protein–protein interaction to block the PD-1/PD-L1 signaling pathway has demonstrated superior anti-tumor efficacy in a variety of solid tumors and has made a profound impact on the field of cancer immunotherapy in recent years. Although the X-ray crystal structure of the PD-1/PD-L1 complex has been solved, the detailed binding mechanism of the PD-1/PD-L1 interaction is not fully understood from a theoretical point of view. In this study, we performed computational alanine scanning on the PD-1/PD-L1 complex to quantitatively identify the hot spots in the PD-1/PD-L1 interaction and characterize its binding mechanisms at the atomic level. To the best of our knowledge, this is the first time that theoretical calculations have been used to systematically and quantitatively predict the hot spots in the PD-1/PD-L1 interaction. We hope that the predicted hot spots and the energy profile of the PD-1/PD-L1 interaction presented in this work can provide guidance for the design of peptide and small molecule drugs targeting PD-1 or PD-L1.

The hot spots quantitatively predicted by the recently developed MM/GBSA/IE method reveal a hydrophobic core in the PD-1/PD-L1 interaction.

In silico structure-based design of enhanced peptide inhibitors targeting RNA polymerase PAN-PB1C interaction

Developing antivirals for influenza A virus (FluA) has become more challenging due to high range of antigenic mutation and increasing numbers of drug-resistant viruses. Finding a selective inhibitor to target highly conserved region of protein-protein interactions interface, thereby increasing its efficiency against drug resistant virus could be highly beneficial. In this study, we used in silico approach to derive FluAPep1 from highly conserved region, PA_N-PB1_C interface and generated 121 FluAPep1 analogues. Interestingly, we found that the FluAPep1 interaction region in the PA_N domain are highly conserved in many FluA subtypes. Especially, FluAPep1 targets two pandemic FluA strains, H1N1/avian/2009 and H3N2/Victoria/1975. All of these FluA subtypes PA_N domain (H1N1/H3N2CAN/H3N2VIC/H7N1/H7N2) were superimposed with PA_N domain from H17N10 and the calculated root mean standards deviations were less than 3 Å. FlexPepDock analysis revealed that FluAPep1 exhibited higher binding affinity (score -246.155) with the PA_N domain. In addition, around 86% of non-hot spot mutated peptides (FluAPep28-122) showed enhanced binding affinity with PA_N domain. ToxinPred analysis confirmed that designed peptides were non-toxic. Thus, FluAPep1 and its analogues has potential to be further developed into an antiviral treatment against FluA infection.

Prediction of Hot Regions in PPIs Based on Improved Local Community Structure Detecting

The hot regions in PPIs are some assembly regions which are composed of the tightly packed HotSpots. The discovery of hot regions helps to understand life activities and has very important value for biological applications. The identification of hot regions is the basis for protein design and cancer prevention. The existing algorithms of predicting hot regions often have some defects, such as low accuracy and unstability. This paper proposes a novel hot region prediction method based on diverse biological characteristics. First, feature evaluation is employed by using an impoved mRMR method. Then, SVM is adopted to create cassification model based on the features selected. In addition, a new clustering algorithm, namely LCSD (Local community structure detecting), is developed to detect and analyze the conformation of hot regions. In the clustering process, the link similarity of protein residues is introduced to handle the boundary nodes. This algorithm can effectively deal with the missing residue nodes and control the local community boundaries. The results indicate that the spatial structure of hot regions can be obtained more effectively, and that our method is more effective than previous methods for precise identification of hot regions.

PPInS: a repository of protein-protein interaction sitesbase

Protein-Protein Interaction Sitesbase (PPInS), a high-performance database of protein-protein interacting interfaces, is presented. The atomic level information of the molecular interaction happening amongst various protein chains in protein-protein complexes (as reported in the Protein Data Bank [PDB]) together with their evolutionary information in Structural Classification of Proteins (SCOPe release 2.06), is made available in PPInS. Total 32468 PDB files representing X-ray crystallized multimeric protein-protein complexes with structural resolution better than 2.5 Å had been shortlisted to demarcate the protein-protein interaction interfaces (PPIIs). A total of 111857 PPIIs with ~32.24 million atomic contact pairs (ACPs) were generated and made available on a web server for on-site analysis and downloading purpose. All these PPIIs and protein-protein interacting patches (PPIPs) involved in them, were also analyzed in terms of a number of residues contributing in patch formation, their hydrophobic nature, amount of surface area they contributed in binding, and their homo and heterodimeric nature, to describe the diversity of information covered in PPInS. It was observed that 42.37% of total PPIPs were made up of 6–20 interacting residues, 53.08% PPIPs had interface area ≤1000 Ų in PPII formation, 82.64% PPIPs were reported with hydrophobicity score of ≤10, and 73.26% PPIPs were homologous to each other with the sequence similarity score ranging from 75–100%. A subset “Non-Redundant Database (NRDB)” of the PPInS containing 2265 PPIIs, with over 1.8 million ACPs corresponding to the 1931 protein-protein complexes (PDBs), was also designed by removing structural redundancies at the level of SCOP superfamily (SCOP release 1.75). The web interface of the PPInS (http://www.cup.edu.in:99/ppins/home.php) offers an easy-to-navigate, intuitive and user-friendly environment, and can be accessed by providing PDB ID, SCOP superfamily ID, and protein sequence.

Arl2-Mediated Allosteric Release of Farnesylated KRas4B from Shuttling Factor PDEδ

Proper localization of Ras proteins at the plasma membrane (PM) is crucial for their functions. To get to the PM, KRas4B and some other Ras family proteins bind to the PDEδ shuttling protein through their farnesylated hypervariable regions (HVRs). The docking of their farnesyl (and to a lesser extent geranylgeranyl) in the hydrophobic pocket of PDEδ's stabilizes the interaction. At the PM, guanosine 5'-triphosphate (GTP)-bound Arf-like protein 2 (Arl2) assists in the release of Ras from the PDEδ. However, exactly how is still unclear. Using all-atom molecular dynamics simulations, we unraveled the detailed mechanism of Arl2-mediated release of KRas4B, the most abundant oncogenic Ras isoform, from PDEδ. We simulated ternary Arl2-PDEδ-KRas4B HVR complexes and observed that Arl2 binding weakens the PDEδ-farnesylated HVR interaction. Our detailed analysis showed that allosteric changes (involving β6 of PDEδ and additional PDEδ residues) compress the hydrophobic PDEδ pocket and push the HVR out. Mutating PDEδ residues that mediate allosteric changes in PDEδ terminates the release process. Mutant Ras proteins are enriched in human cancers, with currently no drugs in the clinics. This mechanistic account may inspire efforts to develop drugs suppressing oncogenic KRas4B release.

Structural insights into pharmacophore-assisted in silico identification of protein-protein interaction inhibitors for inhibition of human toll-like receptor 4 - myeloid differentiation factor-2 (hTLR4-MD-2) complex

Toll-like receptor 4 (TLR4) is a member of Toll-Like Receptors (TLRs) family that serves as a receptor for bacterial lipopolysaccharide (LPS). TLR4 alone cannot recognize LPS without aid of co-receptor myeloid differentiation factor-2 (MD-2). Binding of LPS with TLR4 forms a LPS-TLR4-MD-2 complex and directs downstream signaling for activation of immune response, inflammation and NF-κB activation. Activation of TLR4 signaling is associated with various pathophysiological consequences. Therefore, targeting protein-protein interaction (PPI) in TLR4-MD-2 complex formation could be an attractive therapeutic approach for targeting inflammatory disorders. The aim of present study was directed to identify small molecule PPI inhibitors (SMPPIIs) using pharmacophore mapping-based approach of computational drug discovery. Here, we had retrieved the information about the hot spot residues and their pharmacophoric features at both primary (TLR4-MD-2) and dimerization (MD-2-TLR4*) protein-protein interaction interfaces in TLR4-MD-2 homo-dimer complex using in silico methods. Promising candidates were identified after virtual screening, which may restrict TLR4-MD-2 protein-protein interaction. In silico off-target profiling over the virtually screened compounds revealed other possible molecular targets. Two of the virtually screened compounds (C11 and C15) were predicted to have an inhibitory concentration in μM range after HYDE assessment. Molecular dynamics simulation study performed for these two compounds in complex with target protein confirms the stability of the complex. After virtual high throughput screening we found selective hTLR4-MD-2 inhibitors, which may have therapeutic potential to target chronic inflammatory diseases.

Unraveling the molecular mechanism of interactions of the Rho GTPases Cdc42 and Rac1 with the scaffolding protein IQGAP2

IQ motif-containing GTPase-activating proteins (IQGAPs) are scaffolding proteins playing central roles in cell-cell adhesion, polarity, and motility. The Rho GTPases Cdc42 and Rac1, in their GTP-bound active forms, interact with all three human IQGAPs. The IQGAP-Cdc42 interaction promotes metastasis by enhancing actin polymerization. However, despite their high sequence identity, Cdc42 and Rac1 differ in their interactions with IQGAP. Two Cdc42 molecules can bind to the Ex-domain and the RasGAP site of the GTPase-activating protein (GAP)-related domain (GRD) of IQGAP and promote IQGAP dimerization. Only one Rac1 molecule might bind to the RasGAP site of GRD and may not facilitate the dimerization, and the exact mechanism of Cdc42 and Rac1 binding to IQGAP is unclear. Using all-atom molecular dynamics simulations, site-directed mutagenesis, and Western blotting, we unraveled the detailed mechanisms of Cdc42 and Rac1 interactions with IQGAP2. We observed that Cdc42 binding to the Ex-domain of GRD of IQGAP2 (GRD2) releases the Ex-domain at the C-terminal region of GRD2, facilitating IQGAP2 dimerization. Cdc42 binding to the Ex-domain promoted allosteric changes in the RasGAP site, providing a binding site for the second Cdc42 in the RasGAP site. Of note, the Cdc42 "insert loop" was important for the interaction of the first Cdc42 with the Ex-domain. By contrast, differences in Rac1 insert-loop sequence and structure precluded its interaction with the Ex-domain. Rac1 could bind only to the RasGAP site of apo-GRD2 and could not facilitate IQGAP2 dimerization. Our detailed mechanistic insights help decipher how Cdc42 can stimulate actin polymerization in metastasis.

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

Understanding Insulin Endocrinology in Decapod Crustacea: Molecular Modelling Characterization of an Insulin-Binding Protein and Insulin-Like Peptides in the Eastern Spiny Lobster, Sagmariasus verreauxi

The insulin signalling system is one of the most conserved endocrine systems of Animalia from mollusc to man. In decapod Crustacea, such as the Eastern spiny lobster, Sagmariasus verreauxi (Sv) and the red-claw crayfish, Cherax quadricarinatus (Cq), insulin endocrinology governs male sexual differentiation through the action of a male-specific, insulin-like androgenic gland peptide (IAG). To understand the bioactivity of IAG it is necessary to consider its bio-regulators such as the insulin-like growth factor binding protein (IGFBP). This work has employed various molecular modelling approaches to represent S. verreauxi IGFBP and IAG, along with additional Sv-ILP ligands, in order to characterise their binding interactions. Firstly, we present Sv- and Cq-ILP2: neuroendocrine factors that share closest homology with Drosophila ILP8 (Dilp8). We then describe the binding interaction of the N-terminal domain of Sv-IGFBP and each ILP through a synergy of computational analyses. In-depth interaction mapping and computational alanine scanning of IGFBP_N' highlight the conserved involvement of the hotspot residues Q₆₇, G₇₀, D₇₁, S₇₂, G₉₁, G₉₂, T₉₃ and D₉₄. The significance of the negatively charged residues D₇₁ and D₉₄ was then further exemplified by structural electrostatics. The functional importance of the negative surface charge of IGFBP is exemplified in the complementary electropositive charge on the reciprocal binding interface of all three ILP ligands. When examined, this electrostatic complementarity is the inverse of vertebrate homologues; such physicochemical divergences elucidate towards ligand-binding specificity between Phyla.

Enriching Traditional Protein-protein Interaction Networks with Alternative Conformations of Proteins

Traditional Protein-Protein Interaction (PPI) networks, which use a node and edge representation, lack some valuable information about the mechanistic details of biological processes. Mapping protein structures to these PPI networks not only provides structural details of each interaction but also helps us to find the mutual exclusive interactions. Yet it is not a comprehensive representation as it neglects the conformational changes of proteins which may lead to different interactions, functions, and downstream signalling. In this study, we proposed a new representation for structural PPI networks inspecting the alternative conformations of proteins. We performed a large-scale study by creating breast cancer metastasis network and equipped it with different conformers of proteins. Our results showed that although 88% of proteins in our network has at least two structures in Protein Data Bank (PDB), only 22% of them have alternative conformations and the remaining proteins have different regions saved in PDB. However, using even this small set of alternative conformations we observed a considerable increase in our protein docking predictions. Our protein-protein interaction predictions increased from 54% to 76% using the alternative conformations. We also showed the benefits of investigating structural data and alternative conformations of proteins through three case studies.

Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery

Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.

Small Molecules Engage Hot Spots through Cooperative Binding To Inhibit a Tight Protein-Protein Interaction

Protein-protein interactions drive every aspect of cell signaling, yet only a few small-molecule inhibitors of these interactions exist. Despite our ability to identify critical residues known as hot spots, little is known about how to effectively engage them to disrupt protein-protein interactions. Here, we take advantage of the ease of preparation and stability of pyrrolinone 1, a small-molecule inhibitor of the tight interaction between the urokinase receptor (uPAR) and its binding partner, the urokinase-type plasminogen activator uPA, to synthesize more than 40 derivatives and explore their effect on the protein-protein interaction. We report the crystal structure of uPAR bound to previously discovered pyrazole 3 and to pyrrolinone 12. While both 3 and 12 bind to uPAR and compete with a fluorescently labeled peptide probe, only 12 and its derivatives inhibit the full uPAR·uPA interaction. Compounds 3 and 12 mimic and engage different hot-spot residues on uPA and uPAR, respectively. Interestingly, 12 is involved in a π-cation interaction with Arg-53, which is not considered a hot spot. Explicit-solvent molecular dynamics simulations reveal that 3 and 12 exhibit dramatically different correlations of motion with residues on uPAR. Free energy calculations for the wild-type and mutant uPAR bound to uPA or 12 show that Arg-53 interacts with uPA or with 12 in a highly cooperative manner, thereby altering the contributions of hot spots to uPAR binding. The direct engagement of peripheral residues not considered hot spots through π-cation or salt-bridge interactions could provide new opportunities for enhanced small-molecule engagement of hot spots to disrupt challenging protein-protein interactions.

The interdigitating loop of the enolase superfamily as a specificity binding determinant or 'flying buttress'

BACKGROUND

Enzymes of the enolase superfamily (ENS) are mechanistically diverse, yet share a common partial reaction (abstraction of the α-proton from a carboxylate substrate). While the catalytic machinery responsible for the deprotonation reaction has been conserved, divergent evolution has led to numerous ENS members that catalyze different overall reactions. This rich functional diversity has made the ENS an excellent model system for developing the approaches necessary to validate enzyme function. However, enzymes of the ENS also share a common bidomain structure ((β/α)₇β-barrel domain and α+β capping domain) which makes validation of function from structural information challenging.

SCOPE OF THE REVIEW

This review presents a comparative survey of the structural data obtained over the past decade for enzymes from all seven subgroups that comprise the ENS.

MAJOR CONCLUSIONS

Of the seven ENS subgroups (enolase, mandelate racemase (MR), muconate lactonizing enzyme, β-methylaspartate ammonia lyase, d-glucarate dehydratase, d-mannonate dehydratase (ManD), and galactarate dehydratase 2), only enzymes of the MR and ManD subgroups exhibit an additional feature of structural complexity-an interdigitating loop. This loop emanates from one protomer of a homodimeric pair and penetrates into the adjacent, symmetry-related protomer to either contribute a binding determinant to the active site of the adjacent protomer, or act as a 'flying buttress' to support residues of the active site.

GENERAL SIGNIFICANCE

The analysis presented in this review suggests that the interdigitating loop is the only gross structural element that permits functional distinction between ENS subgroups at the tertiary level of protein structure.

Protein-Protein Interactions (PPIs) as an Alternative to Targeting the ATP Binding Site of Kinase

Most of the developed kinase inhibitor drugs are ATP competitive and suffer from drawbacks such as off-target kinase activity, development of resistance due to mutation in the ATP binding pocket and unfavorable intellectual property situations. Besides the ATP binding pocket, protein kinases have binding sites that are involved in Protein-Protein Interactions (PPIs); these PPIs directly or indirectly regulate the protein kinase activity. Of recent, small molecule inhibitors of PPIs are emerging as an alternative to ATP competitive agents. Rational design of inhibitors for kinase PPIs could be carried out using molecular modeling techniques. In silico tools available for the prediction of hot spot residues and cavities at the PPI sites and the means to utilize this information for the identification of inhibitors are discussed. Moreover, in silico studies to target the Aurora B-INCENP PPI sites are discussed in context. Overall, this chapter provides detailed in silico strategies that are available to the researchers for carrying out structure-based drug design of PPI inhibitors.

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.

Improving hot region prediction by parameter optimization of density clustering in PPI

This paper proposed an optimized algorithm which combines density clustering of parameter selection with feature-based classification for hot region prediction. First, all the residues are classified by SVM to remove non-hot spot residues, then density clustering of parameter selection is used to find hot regions. In the density clustering, this paper studies how to select input parameters. There are two parameters radius and density in density-based incremental clustering. We firstly fix density and enumerate radius to find a pair of parameters which leads to maximum number of clusters, and then we fix radius and enumerate density to find another pair of parameters which leads to maximum number of clusters. Experiment results show that the proposed method using both two pairs of parameters provides better prediction performance than the other method, and compare these two predictive results, the result by fixing radius and enumerating density have slightly higher prediction accuracy than that by fixing density and enumerating radius.