Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK.

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

1D and 2D NMR for KRAS:Ligand Binding.

Fragment-based screening by ligand-observed 1D NMR and binding interface mapping by protein-observed 2D NMR are popular methods used in drug discovery. These methods allow researchers to detect compound binding over a wide range of affinities and offer a simultaneous assessment of solubility, purity, and chemical formula accuracy of the target compounds and the 15N-labeled protein when examined by 1D and 2D NMR, respectively. These methods can be applied for screening fragment binding to the active (GMPPNP-bound) and inactive (GDP-bound) states of oncogenic KRAS mutants.

Profiling Complex RAS-Effector Interactions Using NMR Spectroscopy.

Knowledge of how effectors interact with RAS GTPases is key to understanding how these switch-like proteins function in cells. Effectors bind specifically to GTP-loaded RAS using RAS association (RA) or RAS binding domains (RBDs) that show wide-ranging affinities and thermodynamic characteristics. Both normal development and RAS-induced tumorigenesis depend on multiple distinct effector proteins that are frequently co-expressed and co-localized, suggesting an antagonistic nature to signaling whereby multiple proteins compete for a limited pool of activated GTPase. NMR spectroscopy offers a powerful approach to multiplex effectors and/or regulatory enzymes and quantifies their interaction with RAS, expanding our biophysical and systems-level understanding of RAS signaling in a more integrated and physiologically relevant setting. Here we describe a method to directly quantitate GTPase binding to competing effectors, using wild-type KRAS complex with ARAF and PLCε1 as a model. Unlabeled RBD/RA domains are added simultaneously to isotopically labeled RAS, and peak intensities at chemical shifts characteristic of individually bound domains provide quantitation. Similar competition-based assays can be run with small molecule interactors, GEF/GAP domains, or regulatory enzymes that drive posttranslational modifications. Such efforts bring in vitro interaction experiments in line with more complex cellular environments.

Engineering an artificial catch bond using mechanical anisotropy.

Catch bonds are a rare class of protein-protein interactions where the bond lifetime increases under an external pulling force. Here, we report how modification of anchor geometry generates catch bonding behavior for the mechanostable Dockerin G:Cohesin E (DocG:CohE) adhesion complex found on human gut bacteria. Using AFM single-molecule force spectroscopy in combination with bioorthogonal click chemistry, we mechanically dissociate the complex using five precisely controlled anchor geometries. When tension is applied between residue #13 on CohE and the N-terminus of DocG, the complex behaves as a two-state catch bond, while in all other tested pulling geometries, including the native configuration, it behaves as a slip bond. We use a kinetic Monte Carlo model with experimentally derived parameters to simulate rupture force and lifetime distributions, achieving strong agreement with experiments. Single-molecule FRET measurements further demonstrate that the complex does not exhibit dual binding mode behavior at equilibrium but unbinds along multiple pathways under force. Together, these results show how mechanical anisotropy and anchor point selection can be used to engineer artificial catch bonds.

Viral Receptor-Binding Protein Evolves New Function through Mutations That Cause Trimer Instability and Functional Heterogeneity.

When proteins evolve new activity, a concomitant decrease in stability is often observed because the mutations that confer new activity can destabilize the native fold. In the conventional model of protein evolution, reduced stability is considered a purely deleterious cost of molecular innovation because unstable proteins are prone to aggregation and are sensitive to environmental stressors. However, recent work has revealed that nonnative, often unstable protein conformations play an important role in mediating evolutionary transitions, raising the question of whether instability can itself potentiate the evolution of new activity. We explored this question in a bacteriophage receptor-binding protein during host-range evolution. We studied the properties of the receptor-binding protein of bacteriophage λ before and after host-range evolution and demonstrated that the evolved protein is relatively unstable and may exist in multiple conformations with unique receptor preferences. Through a combination of structural modeling and in vitro oligomeric state analysis, we found that the instability arises from mutations that interfere with trimer formation. This study raises the intriguing possibility that protein instability might play a previously unrecognized role in mediating host-range expansions in viruses.

Enhancement of NETosis by ACE2-cross-reactive anti-SARS-CoV-2 RBD antibodies in patients with COVID-19.

BACKGROUND: High levels of neutrophil extracellular trap (NET) formation or NETosis and autoantibodies are related to poor prognosis and disease severity of COVID-19 patients. Human angiotensin-converting enzyme 2 (ACE2) cross-reactive anti-severe acute respiratory syndrome coronavirus 2 spike protein receptor-binding domain (SARS-CoV-2 RBD) antibodies (CR Abs) have been reported as one of the sources of anti-ACE2 autoantibodies. However, the pathological implications of CR Abs in NET formation remain unknown. METHODS: In this study, we first assessed the presence of CR Abs in the sera of COVID-19 patients with different severity by serological analysis. Sera and purified IgG from CR Abs positive COVID-19 patients as well as a mouse monoclonal Ab (mAb 127) that can recognize both ACE2 and the RBD were tested for their influence on NETosis and the possible mechanisms involved were studied. RESULTS: An association between CR Abs levels and the severity of COVID-19 in 120 patients was found. The CR Abs-positive sera and IgG from severe COVID-19 patients and mAb 127 significantly activated human leukocytes and triggered NETosis, in the presence of RBD. This NETosis, triggered by the coexistence of CR Abs and RBD, activated thrombus-related cells but was abolished when the interaction between CR Abs and ACE2 or Fc receptors was disrupted. We also revealed that CR Abs-induced NETosis was suppressed in the presence of recombinant ACE2 or the Src family kinase inhibitor, dasatinib. Furthermore, we found that COVID-19 vaccination not only reduced COVID-19 severity but also prevented the production of CR Abs after SARS-CoV-2 infection. CONCLUSIONS: Our findings provide possible pathogenic effects of CR Abs in exacerbating COVID-19 by enhancing NETosis, highlighting ACE2 and dasatinib as potential treatments, and supporting the benefit of vaccination in reducing disease severity and CR Abs production in COVID-19 patients.

Computational analysis of affinity dynamics between the variants of SARS-CoV-2 spike protein (RBD) and human ACE-2 receptor.

The novel coronavirus SARS-CoV-2 resulted in a significant worldwide health emergency known as the COVID-19 pandemic. This crisis has been marked by the widespread of various variants, with certain ones causing notable apprehension. In this study, we harnessed computational techniques to scrutinize these Variants of Concern (VOCs), including various Omicron subvariants. Our approach involved the use of protein structure prediction algorithms and molecular docking techniques, we have investigated the effects of mutations within the Receptor Binding Domain (RBD) of SARS-CoV-2 and how these mutations influence its interactions with the human angiotensin-converting enzyme 2 (hACE-2) receptor. Further we have predicted the structural alterations in the RBD of naturally occurring SARS-CoV-2 variants using the tr-Rosetta algorithm. Subsequent docking and binding analysis employing HADDOCK and PRODIGY illuminated crucial interactions occurring at the Receptor-Binding Motif (RBM). Our findings revealed a hierarchy of increased binding affinity between the human ACE2 receptor and the various RBDs, in the order of wild type (Wuhan-strain) < Beta < Alpha < Gamma < Omicron-B.1.1.529 < Delta < Omicron-BA.2.12.1 < Omicron-BA.5.2.1 < Omicron-BA.1.1. Notably, Omicron-BA.1.1 demonstrated the highest binding affinity of -17.4 kcal mol-1 to the hACE2 receptor when compared to all the mutant complexes. Additionally, our examination indicated that mutations occurring in active residues of the Receptor Binding Domain (RBD) consistently improved the binding affinity and intermolecular interactions in all mutant complexes. Analysis of the differences among variants has laid a foundation for the structure-based drug design targeting the RBD region of SARS-CoV-2.

Interaction of T-cell-specific adapter protein with Src- and Tec-family kinases.

Adapter proteins are flexible and dynamic modulators of cellular signalling that are important for immune cell function. One of these, the T-cell-specific adapter protein (TSAd), interacts with the non-receptor tyrosine kinases Src and Lck of the Src family kinases (SFKs) and Itk of the Tec family kinases (TFKs). Three tyrosine residues in the TSAd C-terminus are phosphorylated by Lck and serve as docking sites for the Src homology 2 (SH2) domains of Src and Lck. The TSAd proline-rich region (PRR) binds to the Src homology 3 (SH3) domains found in Lck, Src and Itk. Despite known interactors, the role TSAd plays in cellular signalling remains largely unknown. TSAd's ability to bind both SFKs and TFKs may point to its function as a general scaffold for both kinase families. Using GST-pulldown as well as peptide array experiments, we found that both the SH2 and SH3 domains of the SFKs Fyn and Hck, as well as the TFKs Tec and Txk, interact with TSAd. This contrasts with Itk, which interacts with TSAd only through its SH3 domain. Although our analysis showed that TSAd is both co-expressed and may interact with Fyn, we were unable to co-precipitate Fyn with TSAd from Jurkat cells, as detected by Western blotting and affinity purification mass spectrometry. This may suggest that TSAd-Fyn interaction in intact cells may be limited by other factors, such as the subcellular localization of the two molecules or the co-expression of competing binding partners.

Computational methods in the analysis of SARS-CoV-2 in mammals: A systematic review of the literature.

SARS-CoV-2 is an enveloped RNA virus that causes severe respiratory illness in humans and animals. It infects cells by binding the Spike protein to the host's angiotensin-converting enzyme 2 (ACE2). The bat is considered the natural host of the virus, and zoonotic transmission is a significant risk and can happen when humans come into close contact with infected animals. Therefore, understanding the interconnection between human, animal, and environmental health is important to prevent and control future coronavirus outbreaks. This work aimed to systematically review the literature to identify characteristics that make mammals suitable virus transmitters and raise the main computational methods used to evaluate SARS-CoV-2 in mammals. Based on this review, it was possible to identify the main factors related to transmissions mentioned in the literature, such as the expression of ACE2 and proximity to humans, in addition to identifying the computational methods used for its study, such as Machine Learning, Molecular Modeling, Computational Simulation, between others. The findings of the work contribute to the prevention and control of future outbreaks, provide information on transmission factors, and highlight the importance of advanced computational methods in the study of infectious diseases that allow a deeper understanding of transmission patterns and can help in the development of more effective control and intervention strategies.

Perspectives on Protein-Nanoparticle Interactions at the In Vivo Level.

The distinct features of nanoparticles have provided a vast opportunity of developing new diagnosis and therapy strategies for miscellaneous diseases. Although a few nanomedicines are available in the market or in the translation stage, many important issues are still unsolved. When entering the body, nanomaterials will be quickly coated by proteins from their surroundings, forming a corona on their surface, the so-called protein corona. Studies have shown that the protein corona has many important biological implications, particularly at the in vivo level. For example, they can promote the immune system to rapidly clear these outer materials and prevent nanoparticles from playing their designed role in therapy. In this Perspective, the available techniques for characterizing protein-nanoparticle interactions are critically summarized. Effects of nanoparticle properties and environmental factors on protein corona formation, which can further regulate the in vivo fate of nanoparticles, are highlighted and discussed. Moreover, recent progress on the biomedical application of protein corona-engineered nanoparticles is introduced, and future directions for this important yet challenging research area are also briefly discussed.

Structure-based discovery of small molecule inhibitors of FKBP51-Hsp90 protein-protein interaction.

The heat shock protein 90 kDa (Hsp90) molecular chaperone machinery is responsible for the folding and activation of hundreds of important clients such as kinases, steroid hormone receptors, transcription factors, etc. This process is dynamically regulated in an ATP-dependent manner by Hsp90 co-chaperones including a group of tetratricopeptide (TPR) motif proteins that bind to the C-terminus of Hsp90. Among these TPR containing co-chaperones, FK506-binding protein 51 kDa (FKBP51) is reported to play an important role in stress-related pathologies, psychiatric disorders, Alzheimer's disease, and cancer, making FKBP51-Hsp90 interaction a potential therapeutic target. In this study, we report identification of potent and selective inhibitors of FKBP51-Hsp90 protein-protein interaction using a structure-based virtual screening approach. Upon in vitro evaluation, the identified hits show a considerable degree of selectivity towards FKBP51 over other TPR proteins, particularly for highly homologous FKBP52. Tyr355 of FKBP51 emerged as an important contributor to inhibitor's specificity. Additionally, we demonstrate the impact of these inhibitors on cellular energy metabolism, and neurite outgrowth, which are subjects of FKBP51 regulation. Overall, the results from this study highlight a novel pharmacological approach towards regulation of FKBP51 function and more generally, Hsp90 function via its interaction with TPR co-chaperones.

The Myc-associated zinc finger protein epigenetically controls expression of interferon-γ-stimulated genes by recruiting STAT1 to chromatin.

The MYC-Associated Zinc Finger Protein (MAZ) plays important roles in chromatin organization and gene transcription regulation. Dysregulated expression of MAZ causes diseases, such as glioblastoma, breast cancer, prostate cancer, and liposarcoma. Previously, it has been reported that MAZ controls the proinflammatory response in colitis and colon cancer via STAT3 signaling, suggesting that MAZ is involved in regulating immunity-related pathways. However, the molecular mechanism underlying this regulation remains elusive. Here, we investigate the regulatory effect of MAZ on interferon-gamma (IFN-γ)-stimulated genes via STAT1, a protein that plays an essential role in immune responses to viral, fungal, and mycobacterial pathogens. We demonstrate that about 80% of occupied STAT1-binding sites colocalize with occupied MAZ-binding sites in HAP1/K562 cells after IFN-γ stimulation. MAZ depletion significantly reduces STAT1 binding in the genome. By analyzing genome-wide gene expression profiles in the RNA-Seq data, we show that MAZ depletion significantly suppresses a subset of the immune response genes, which include the IFN-stimulated genes IRF8 and Absent in Melanoma 2. Furthermore, we find that MAZ controls expression of the immunity-related genes by changing the epigenetic landscape in chromatin. Our study reveals an important role for MAZ in regulating immune-related gene expression.

ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.

Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.

Molecular dynamics simulations to explore the binding mode between the amyloid-ß protein precursor (APP) and adaptor protein Mint2.

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative disease management, with limited therapeutic options available for its prevention and treatment. At the heart of AD pathogenesis is the amyloid-ß (Aß) protein precursor (APP), with the interaction between APP and the adaptor protein Mint2 being crucial. Despite previous explorations into the APP-Mint2 interaction, the dynamic regulatory mechanisms by which Mint2 modulates APP binding remain poorly understood. This study undertakes molecular dynamics simulations across four distinct systems-free Mint2, Mint2 bound to APP, a mutant form of Mint2, and the mutant form bound to APP-over an extensive 400 ns timeframe. Our findings reveal that the mutant Mint2 experiences significant secondary structural transformations, notably the formation of an α-helix in residues S55-K65 upon APP binding, within the 400 ns simulation period. Additionally, we observed a reduction in the active pocket size of the mutant Mint2 compared to its wild-type counterpart, enhancing its APP binding affinity. These insights hold promise for guiding the development of novel inhibitors targeting the Mints family, potentially paving the way for new therapeutic strategies in AD prevention and treatment.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Fuzzy recognition by the prokaryotic transcription factor HigA2 from Vibrio cholerae.

Disordered protein sequences can exhibit different binding modes, ranging from well-ordered folding-upon-binding to highly dynamic fuzzy binding. The primary function of the intrinsically disordered region of the antitoxin HigA2 from Vibrio cholerae is to neutralize HigB2 toxin through ultra-high-affinity folding-upon-binding interaction. Here, we show that the same intrinsically disordered region can also mediate fuzzy interactions with its operator DNA and, through interplay with the folded helix-turn-helix domain, regulates transcription from the higBA2 operon. NMR, SAXS, ITC and in vivo experiments converge towards a consistent picture where a specific set of residues in the intrinsically disordered region mediate electrostatic and hydrophobic interactions while "hovering" over the DNA operator. Sensitivity of the intrinsically disordered region to scrambling the sequence, position-specific contacts and absence of redundant, multivalent interactions, point towards a more specific type of fuzzy binding. Our work demonstrates how a bacterial regulator achieves dual functionality by utilizing two distinct interaction modes within the same disordered sequence.

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning.

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein-Carbohydrate Binding Interfaces.

The Protein Structure Transformer (PeSTo), a geometric transformer, has exhibited exceptional performance in predicting protein-protein binding interfaces and distinguishing interfaces with nucleic acids, lipids, small molecules, and ions. In this study, we introduce PeSTo-Carbs, an extension of PeSTo specifically engineered to predict protein-carbohydrate binding interfaces. We evaluate the performance of this approach using independent test sets and compare them with those of previous methods. Furthermore, we highlight the model's capability to specialize in predicting interfaces involving cyclodextrins, a biologically and pharmaceutically significant class of carbohydrates. Our method consistently achieves remarkable accuracy despite the scarcity of available structural data for cyclodextrins.

Pathogenic mutations of human phosphorylation sites affect protein-protein interactions.

Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites.

Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions.