Characterization of protein-protein interfaces

Design of an Fc Mutation to Abrogate Fcγ Receptor Binding Based on Residue Interaction Network Analysis

Immunoglobulins mediate their immune responses through interactions with Fc γ-receptors (FcγRs) on immune cells, triggering crucial responses such as antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP). While enhancing these interactions can be beneficial, in certain therapeutic scenarios, such as cytokine or receptor blockade therapies, it is critical to reduce FcγR binding to avoid adverse immune reactions. This study aims to design negative mutations in the Fc region to reduce Fcγ receptor binding based on the residue interaction network analysis. The mutation sites of Fc were targeted through betweenness centrality analysis, and mutations were designed by focusing on hydrophobic to hydrophilic residue changes. The negative effect of the designed mutants on binding affinity was verified by previous reports and binding experiments. From this study, we identified a new Fc variant candidate (V263(B)D) that lacks a binding affinity for Fcγ receptors. This research highlights a strategic approach for designing Fc mutations that effectively reduce immune activation, which may be valuable in therapeutic contexts, where immune response moderation is crucial.

A hidden cysteine in Fis1 targeted to prevent excessive mitochondrial fission and dysfunction under oxidative stress

Fis1-mediated mitochondrial localization of Drp1 and excessive mitochondrial fission occur in human pathologies associated with oxidative stress. However, it is not known how Fis1 detects oxidative stress and what structural changes in Fis1 enable mitochondrial recruitment of Drp1. We find that conformational change involving α1 helix in Fis1 exposes its only cysteine, Cys41. In the presence of oxidative stress, the exposed Cys41 in activated Fis1 forms a disulfide bridge and the Fis1 covalent homodimers cause increased mitochondrial fission through increased Drp1 recruitment to mitochondria. Our discovery of a small molecule, SP11, that binds only to activated Fis1 by engaging Cys41, and data from genetically engineered cell lines lacking Cys41 strongly suggest a role of Fis1 homodimerization in Drp1 recruitment to mitochondria and excessive mitochondrial fission. The structure of activated Fis1-SP11 complex further confirms these insights related to Cys41 being the sensor for oxidative stress. Importantly, SP11 preserves mitochondrial integrity and function in cells during oxidative stress and thus may serve as a candidate molecule for the development of treatment for diseases with underlying Fis1-mediated mitochondrial fragmentation and dysfunction.

Anaplastic lymphoma kinase enhances Wnt signaling through R-spondin: A new dimension to ALK-mediated oncogenesis

Anaplastic lymphoma kinase receptor (ALK) is a receptor tyrosine kinase that plays a key role in the progression of several cancers and is activated by ligands such as ALKAL1 and ALKAL2. To identify additional molecules that interact with ALK, we constructed comprehensive genetic and molecular level networks. Notably, our study identified R-spondins, growth factors known to enhance Wnt signaling, as novel interacting partners of ALK. Protein-protein docking studies revealed that R-spondins bind to the TNF-like and EGF-like domains of ALK, which are critical for the interaction of ALK with its known ligand ALKAL2. These docking outcomes were further validated by molecular dynamics simulations, and approximate binding affinity calculations that confirmed the stability and conformational behavior of the ALK and R-spondin complex. These in silico findings indicate a strong interaction between ALK and R-spondins. To investigate whether this interaction influences Wnt signaling in vitro, we conducted a Wnt signaling reporter assay (TOP Flash/FOP Flash) in neuroblastoma cells by introducing Rspo2, Wnt3a, and crizotinib, an ALK inhibitor. The results showed a decrease in the TOP/FOP ratio when ALK was inhibited. Collectively, our study reveals a novel role for ALK in enhancing Wnt signaling via R-spondins, providing new dimension into ALK-mediated oncogenesis.

In silico studies provide new structural insights into trans-dimerization of β1 and β2 subunits of the Na+, K+-ATPase

The Na+, K+-ATPase is an electrogenic transmembrane pump located in the plasma membrane of all animal cells. It is a dimeric protein composed of α and β subunits and has a third regulatory subunit (γ) belonging to the FXYD family. This pump plays a key role in maintaining low concentration of sodium and high concentration of potassium intracellularly. The α subunit is the catalytic one while the β subunit is important for the occlusion of the K+ ions and plays an essential role in trafficking of the functional αβ complex of Na+, K+-ATPase to the plasma membrane. Interestingly, the β1 and β2 (AMOG) isoforms of the β subunit, function as cell adhesion molecules in epithelial cells and astrocytes, respectively. Early experiments suggested a heterotypic adhesion for the β2. Recently, we reported a homotypic trans-interaction between β2-subunits expressed in CHO cells. In this work we use In Silico methods to analyze the physicochemical properties of the putative homophilic trans-dimer of β2 subunits and provide insights about the trans-dimerization interface stability. Our structural analysis predicts a molecular recognition mechanism of a trans-dimeric β2 - β2 subunit and permits designing experiments that will shed light upon possible homophilic interactions of β2 subunits in the nervous system.

The identification of functional regions of MEK1 using CRISPR tiling screens

CRISPR tiling screen is a powerful tool to identify protein regions relevant to its biological function. Understanding the functional relevance of the regions of target protein is of great help for structure-based drug discovery. Studying the drug resistance mechanisms of small-molecule inhibitors is important for the development and clinical application of the compounds. Using MEK1 and MEK inhibitors as example here, we demonstrate the utility of CRISPR tiling to identify regions essential for cancer cell viability and regions where mutations are resistant to MEK inhibitors. We study the drug resistance mechanisms of the regions and discussed the potential, as well as limitations, of applying the technology to drug development. Our findings demonstrate the value and prompt the utilization of CRISPR tiling technology in structure-based drug discovery.

Structural Descriptors for Subunit Interface Regions in Homodimers: Effect of Lipid Membrane and Secondary Structure Type

A total of 1311 homodimers were collected and analyzed in three different categories to highlight the impact of lipid environment and secondary structure type: 422 cytoplasmic α-helix, 411 cytoplasmic β-strand, and 478 membrane complexes. Structural features of the interface connecting two monomers were investigated and compared to those of the non-interface surface. Every residue on the surface of each monomer was explored based on four attributes: solvent-accessible surface area (SASA), protrusion index (Cx), surface planarity, and surface roughness. SASA and Cx distribution profiles clearly distinguished the interface from the surface in all categories, where the rim of the interface displayed higher SASA and Cx values than the rest of the surface. Surface residues in membrane complexes protruded less than cytoplasmic ones due to the hydrophobic environment, and consequently, the difference between surface and interface residues became less noticeable in that category. Cytoplasmic β-strand complexes displayed markedly lower SASA at the interface core than at the surface. The major distinction between the surface and interface was achieved through surface roughness, which displayed significantly higher values for the interface than the surface, especially in cytoplasmic complexes. Clearly, a surface which is relatively rugged favors the association of two monomers through multiple van der Waals interactions and hydrogen-bond formations. Another structural descriptor with strong distinguishing ability was surface planarity, which was higher at the interface than at the non-interface surface. Surface flatness would eventually facilitate the interconnectedness of an interface with a network of residue pairs bridging two complementary surfaces. Analysis of contact pairs revealed that hydrophobic pairs have the highest frequency of occurrence in the lipid environment of membrane complexes. However, despite the scarcity of polar residues at the interface, the likelihood of observing a contact between polar residues was markedly higher than that of hydrophobic ones.

Exploring neural networks to uncover information-richer features for protein interaction prediction

Moving in a crowded cellular environment, proteins have to recognize and bind to each other with high specificity. This specificity reflects in a combination of geometric and chemical complementarities at the core of interacting regions that ultimately influences binding stability. Exploiting such peculiar complementarity patterns, we recently developed CIRNet, a neural network architecture capable of identifying pairs of protein core interacting residues and assisting docking algorithms by rescaling the proposed poses. Here, we present a detailed analysis of the geometric and chemical descriptors utilized by CIRNet, investigating its decision-making process to gain deeper insights into the interactions governing protein-protein binding and their interdependence. Specifically, we quantitatively assess (i) the relative importance of chemical and physical features in network training and (ii) their interplay at protein interfaces. We show that shape and hydrophobic-hydrophilic complementarities contain the most predictive information about the classification outcome. Electrostatic complementarity alone does not achieve high classification accuracy but is required to boost learning. Ultimately, our findings suggest that identifying the most information-dense features may enhance our understanding of the mechanisms driving protein-protein interactions at core interfaces.

Unveiling Interaction Signatures Across Viral Pathogens through VASCO: Viral Antigen-Antibody Structural COmplex dataset

Viral antigen-antibody (Ag-Ab) interactions shape immune responses, drive pathogen neutralization, and inform vaccine strategies. Understanding their structural basis is crucial for predicting immune recognition, optimizing immunogen design to induce broadly neutralizing antibodies (bnAbs), and developing antiviral therapeutics. However, curated structural benchmarks for viral Ag-Ab interactions remain scarce. To address this, we present VASCO (Viral Antibody-antigen Structural COmplex dataset), a high-resolution, non-redundant collection of ~1225 viral Ag-Ab complexes sourced from the Protein Data Bank (PDB) and refined via energy minimization. Spanning Coronaviruses, Influenza, Ebola, HIV, and others, VASCO provides a comprehensive structural reference for viral immune recognition. By comparing VASCO against general protein-protein interactions (GPPI), we identify distinct sequence and structural features that define viral Ag-Ab binding. While conventional descriptors show broad similarities across datasets, deeper analyses reveal key sequence-space interactions, secondary structure preferences, and manifold-derived latent features that distinguish viral complexes. These insights highlight the limitations of GPPI-trained predictive models and the need for specialized computational frameworks. VASCO serves as a critical resource for advancing viral immunology, improving predictive modeling, and guiding immunogen design to elicit protective antibody responses. By bridging sequence and structural immunological datasets, VASCO should enable better docking, affinity prediction, and antiviral therapeutic development-key to pandemic preparedness and emerging pathogen response.

Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues

Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregates formation. Despite the many advancements made so far, the performance of docking protocols is deeply dependent on their capability to identify binding regions. From this, the importance of developing low-cost and computationally efficient methods in this field. We present an integrated novel protocol mainly based on compact modeling of protein surface patches via sets of orthogonal polynomials to identify regions of high shape/electrostatic complementarity. By incorporating both hydrophilic and hydrophobic contributions, we define new binding matrices, which serve as effective inputs for training a neural network. In this work, we propose a new Neural Network (NN)-based architecture, Core Interacting Residues Network (CIRNet), which achieves a performance in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC) of approximately 0.87 in identifying pairs of core interacting residues on a balanced data set. In a blind search for core interacting residues, CIRNet distinguishes them from random decoys with an ROC AUC of 0.72. We test this protocol to enhance docking algorithms by filtering the proposed poses, addressing one of the still open problems in computational biology. Notably, when applied to the top ten models from three widely used docking servers, CIRNet improves docking outcomes, significantly reducing the average RMSD between the selected poses and the native state. Compared to another state-of-the-art tool for rescaling docking poses, CIRNet more efficiently identified the worst poses generated by the three docking servers under consideration and achieved superior rescaling performance in two cases.

Underlying features for the enhanced electrostatic strength of the extremophilic malate dehydrogenase interface salt-bridge compared to the mesophilic one

Malate dehydrogenase (MDH) exists in multimeric form in normal and extreme solvent conditions where residues of the interface are involved in specific interactions. The interface salt-bridge (ISB) and its microenvironment (ME) residues may have a crucial role in the stability and specificity of the interface. To gain insight into this, we have analyzed 218 ISBs from 42 interfaces of 15 crystal structures along with their sequences. Comparative analyses demonstrate that the ISB strength is ∼30 times greater in extremophilic cases than that of the normal one. To this end, the interface residue propensity, ISB design and pair selection, and ME-residue's types, i.e., type-I and type-II, are seen to be intrinsically involved. Although Type-I is a common type, Type-II appears to be extremophile-specific, where the net ME-residue count is much lower with an excessive net ME-energy contribution, which seems to be a novel interface compaction strategy. Furthermore, the interface strength can be enhanced by selecting the desired mutant from the net-energy profile of all possible mutations of an unfavorable ME-residue. The study that applies to other similar systems finds applications in protein-protein interaction and protein engineering.

Multiplex Trifluoromethyl and Hydroxyl Radical Chemistry Enables High-Resolution Protein Footprinting

Hydroxyl radical-based protein footprinting (HRPF) coupled with mass spectrometry is a valuable medium-resolution technique in structural biology, facilitating the assessment of protein structure and molecular-level interactions in solution conditions. In HRPF with X-rays (XFP), hydroxyl radicals generated by water radiolysis covalently label multiple amino acid (AA) side chains. However, HRPF technologies face challenges in achieving their full potential due to the broad (>103) dynamic range of AA reactivity with •OH and difficulty in detecting slightly modified residues, most notably in peptides with highly reactive residues like methionine, or where all residues have low •OH reactivities. To overcome this limitation, we developed a multiplex labeling chemistry that utilizes both CF3 radicals (•CF3) produced from a trifluoromethylation (TFM) reagent and OH radicals (•OH), under controlled and optimized radiolysis doses generated by X-rays. We optimized the dual •CF3/•OH chemistry using model peptides and proteins, thereby extending the existing •OH labeling platform to incorporate simultaneous •CF3 labeling. We labeled >50% of the protein sequence and >80% of protein solvent-accessible AAs via multiplex TFM labeling resulting in high-resolution footprinting, primarily by enhancing the labeling of AAs with low •OH reactivity via the •CF3 channel, while labeling moderate and highly •OH-reactive AAs in both •CF3 and •OH channels. Moreover, the low reactivity of methionine with •CF3 enabled the detection and quantification of additional AAs labeled by •CF3 within methionine-containing peptides. Finally, we found that the solvent accessibility of protein AAs directly correlated with •CF3 labeling, demonstrating that multiplex TFM labeling enables a high-resolution assessment of molecular interactions for enhanced HRPF.

Target-Templated Construction of Functional Proteomimetics Using Photo-Foldamer Libraries

Current methods for proteomimetic engineering rely on structure-based design. Here we describe a design strategy that allows the construction of proteomimetics against challenging targets without a priori characterization of the target surface. Our approach employs (i) a 100-membered photoreactive foldamer library, the members of which act as local surface mimetics, and (ii) the subsequent affinity maturation of the primary hits using systems chemistry. Two surface-oriented proteinogenic side chains drove the interactions between the short helical foldamer fragments and the proteins. Diazirine-based photo-crosslinking was applied to sensitively detect and localize binding even to shallow and dynamic patches on representatively difficult targets. Photo-foldamers identified functionally relevant protein interfaces, allosteric and previously unexplored targetable regions on the surface of STAT3 and an oncogenic K-Ras variant. Target-templated dynamic linking of foldamer hits resulted in two orders of magnitude affinity improvement in a single step. The dimeric K-Ras ligand mimicked protein-like catalytic functions. The photo-foldamer approach thus enables the highly efficient mapping of protein-protein interaction sites and provides a viable starting point for proteomimetic ligand development without a priori structural hypotheses.

ProBID-Net: a deep learning model for protein-protein binding interface design

Protein-protein interactions are pivotal in numerous biological processes. The computational design of these interactions facilitates the creation of novel binding proteins, crucial for advancing biopharmaceutical products. With the evolution of artificial intelligence (AI), protein design tools have swiftly transitioned from scoring-function-based to AI-based models. However, many AI models for protein design are constrained by assuming complete unfamiliarity with the amino acid sequence of the input protein, a feature most suited for de novo design but posing challenges in designing protein-protein interactions when the receptor sequence is known. To bridge this gap in computational protein design, we introduce ProBID-Net. Trained using natural protein-protein complex structures and protein domain-domain interface structures, ProBID-Net can discern features from known target protein structures to design specific binding proteins based on their binding sites. In independent tests, ProBID-Net achieved interface sequence recovery rates of 52.7%, 43.9%, and 37.6%, surpassing or being on par with ProteinMPNN in binding protein design. Validated using AlphaFold-Multimer, the sequences designed by ProBID-Net demonstrated a close correspondence between the design target and the predicted structure. Moreover, the model's output can predict changes in binding affinity upon mutations in protein complexes, even in scenarios where no data on such mutations were provided during training (zero-shot prediction). In summary, the ProBID-Net model is poised to significantly advance the design of protein-protein interactions.

The CTR hydrophobic residues of Nem1 catalytic subunit are required to form a protein phosphatase complex with Spo7 to activate yeast Pah1 PA phosphatase

The Nem1-Spo7 phosphatase complex plays a key role in lipid metabolism as an activator of Pah1 phosphatidate phosphatase, which produces diacylglycerol for the synthesis of triacylglycerol and membrane phospholipids. For dephosphorylation of Pah1, the Nem1 catalytic subunit requires Spo7 for the recruitment of the protein substrate and interacts with the regulatory subunit through its conserved region (residues 251-446). In this work, we found that the Nem1 C-terminal region (CTR) (residues 414-436), which flanks the haloacid dehalogenase-like catalytic domain (residues 251-413), contains the conserved hydrophobic residues (L414, L415, L417, L418, L421, V430, L434, and L436) that are necessary for the complex formation with Spo7. AlphaFold predicts that some CTR residues of Nem1 interact with Spo7 conserved regions, whereas some residues interact with the haloacid dehalogenase-like domain. By site-directed mutagenesis, Nem1 variants were constructed to lack (Δ(414-446)) or substitute alanines (8A) and arginines (8R) for the hydrophobic residues. When co-expressed with Spo7, the CTR variants of Nem1 did not form a complex with Spo7. In addition, the Nem1 variants were incapable of catalyzing the dephosphorylation of Pah1 in the presence of Spo7. Moreover, the Nem1 variants expressed in nem1Δ cells did not complement the phenotypes characteristic of a defect in the Nem1-Spo7/Pah1 phosphatase cascade function (e.g., lipid synthesis, lipid droplet formation, and phospholipid biosynthetic gene expression). These findings support that Nem1 interacts with Spo7 through its CTR hydrophobic residues to form a phosphatase complex for catalytic activity and physiological functions.

Noncontact 3D Bioprinting of Proteinaceous Microarrays for Highly Sensitive Immunofluorescence Detection within Clinical Samples

Immunofluorescence assays are extensively used for the detection of disease-associated biomarkers within patient samples for direct diagnosis. Unfortunately, these 2D microarrays suffer from low repeatability and fail to attain the low limits of detection (LODs) required to accurately discern disease progression for clinical monitoring. While three-dimensional microarrays with increased biorecognition molecule density stand to circumvent these limitations, their viscous component materials are not compatible with current microarray fabrication protocols. Herein, we introduce a platform for 3D microarray bioprinting, wherein a two-step printing approach enables the high-throughput fabrication of immunosorbent hydrogels. The hydrogels are composed entirely of cross-linked proteins decorated with clinically relevant capture antibodies. Compared to two-dimensional microarrays, these proteinaceous microarrays offer 3-fold increases in signal intensity. When tested with clinically relevant biomarkers, ultrasensitive single-plex and multiplex detection of interleukin-6 (LOD 0.3 pg/mL) and tumor necrosis factor receptor 1 (LOD 1 pg/mL) is observed. When challenged with clinical samples, these hydrogel microarrays consistently discern elevated levels of interleukin-6 in blood plasma derived from patients with systemic blood infections. Given their easy-to-implement, high-throughput fabrication, and ultrasensitive detection, these three-dimensional microarrays will enable better clinical monitoring of disease progression, yielding improved patient outcomes.

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

Direct Inhibition of the YAP : TEAD Interaction: An Unprecedented Drug Discovery Challenge

The Hippo pathway, which is key in organ morphogenesis, is frequently deregulated in cancer. The TEAD (TEA domain family member) transcription factors are the most distal elements of this pathway, and their activity is regulated by proteins such as YAP (Yes-associated protein). The identification of inhibitors of the YAP : TEAD interaction is one approach to develop novel anticancer drugs: the first clinical candidate (IAG933) preventing the association between these two proteins by direct competition has just been reported. The discovery of this molecule was particularly challenging because the interface between these two proteins is large (~3500 Å2 buried in complex formation) and made up of distinct contact areas. The most critical of these involves an omega-loop (Ω-loop), a secondary structure element rarely found in protein-protein interactions. This review summarizes how the knowledge gained from structure-function studies of the interaction between the Ω-loop of YAP and TEAD was used to devise the strategy to identify potent low-molecular weight compounds that show a pronounced anti-tumor effect.

Structural insights into the initiation of free radical formation in the Class Ib ribonucleotide reductases in Mycobacteria

Class I ribonucleotide reductases consisting of α and β subunits convert ribonucleoside diphosphates to deoxyribonucleoside diphosphates involving an intricate free radical mechanism. The generation of free radicals in the Class Ib ribonucleotide reductases is mediated by di-manganese ions in the β subunits and is externally assisted by flavodoxin-like NrdI subunit. This is unlike Class Ia ribonucleotide reductases, where the free radical generation is initiated at its di-iron centre in the β subunits with no external support from another subunit. Class 1b ribonucleotide reductase complex is an essential enzyme complex in the human pathogen Mycobacterium tuberculosis and its structural details are largely unknown. In this study we have determined the crystal structures of Mycobacterial NrdI in oxidised and reduced forms, and similarly those of NrdF2:NrdI complexes. These structures provide detailed atomic view of the mechanism of free radical generation in the β subunit in this pathogen. We observe a well-formed channel in NrdI from the surface leading to the buried FMN moiety and propose that oxygen molecule accesses FMN through it. The oxygen molecule is further converted to a superoxide ion upon electron transfer at the FMN moiety. Similarly, a path for superoxide radical transfer between NrdI and NrdF2 is also observed. The oxidation of Mn(II) in NrdF2I to high valent oxidation state (either Mn(III) or Mn(IV) assisted by the reduced FMN site was evidently confirmed by EPR studies. SEC-MALS and low resolution cryo-EM map indicate unusual stoichiometry of 2:1 in the M. tuberculosis NrdF2I complex. A density close to Tyr 110 at a distance <2.3 Å is observed, which we interpret as OH group. Overall, the study therefore provides important clues on the initiation of free radical generation in the β subunit of the ribonucleotide reductase complex in M. tuberculosis.

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning

Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting the effects of mutations on these complex interactions. To address this, we present DDMut-PPI, a deep learning model that efficiently and accurately predicts changes in PPI binding free energy upon single and multiple point mutations. Building on the robust Siamese network architecture with graph-based signatures from our prior work, DDMut, the DDMut-PPI model was enhanced with a graph convolutional network operated on the protein interaction interface. We used residue-specific embeddings from ProtT5 protein language model as node features, and a variety of molecular interactions as edge features. By integrating evolutionary context with spatial information, this framework enables DDMut-PPI to achieve a robust Pearson correlation of up to 0.75 (root mean squared error: 1.33 kcal/mol) in our evaluations, outperforming most existing methods. Importantly, the model demonstrated consistent performance across mutations that increase or decrease binding affinity. DDMut-PPI offers a significant advancement in the field and will serve as a valuable tool for researchers probing the complexities of protein interactions. DDMut-PPI is freely available as a web server and an application programming interface at https://biosig.lab.uq.edu.au/ddmut_ppi.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

Alternate conformations found in protein structures implies biological functions: A case study using cyclophilin A

Protein dynamics linked to numerous biomolecular functions, such as ligand binding, allosteric regulation, and catalysis, must be better understood at the atomic level. Reactive atoms of key residues drive a repertoire of biomolecular functions by flipping between alternate conformations or conformational substates, seldom found in protein structures. Probing such sparsely sampled alternate conformations would provide mechanistic insight into many biological functions. We are therefore interested in evaluating the instance of amino acids adopted alternate conformations, either in backbone or side-chain atoms or in both. Accordingly, over 70000 protein structures appear to contain alternate conformations only 'A' and 'B' for any atom, particularly the instance of amino acids that adopted alternate conformations are more for Arg, Cys, Met, and Ser than others. The resulting protein structure analysis depicts that amino acids with alternate conformations are mainly found in the helical and β-regions and are often seen in high-resolution X-ray crystal structures. Furthermore, a case study on human cyclophilin A (CypA) was performed to explain the pre-existing intrinsic dynamics of catalytically critical residues from the CypA and how such intrinsic dynamics perturbed upon Ser99Thr mutation using molecular dynamics simulations on the ns-μs timescale. Simulation results demonstrated that the Ser99Thr mutation had impaired the alternate conformations or the catalytically productive micro-environment of Phe113, mimicking the experimentally observed perturbation captured by X-ray crystallography. In brief, a deeper comprehension of alternate conformations adopted by the amino acids may shed light on the interplay between protein structure, dynamics, and function.

Colabind: A Cloud-Based Approach for Prediction of Binding Sites Using Coarse-Grained Simulations with Molecular Probes

Binding site prediction is a crucial step in understanding protein-ligand and protein-protein interactions (PPIs) with broad implications in drug discovery and bioinformatics. This study introduces Colabind, a robust, versatile, and user-friendly cloud-based approach that employs coarse-grained molecular dynamics simulations in the presence of molecular probes, mimicking fragments of drug-like compounds. Our method has demonstrated high effectiveness when validated across a diverse range of biological targets spanning various protein classes, successfully identifying orthosteric binding sites, as well as known druggable allosteric or PPI sites, in both experimentally determined and AI-predicted protein structures, consistently placing them among the top-ranked sites. Furthermore, we suggest that careful inspection of the identified regions with a high affinity for specific probes can provide valuable insights for the development of pharmacophore hypotheses. The approach is available at https://github.com/porekhov/CG_probeMD.

The Multienzyme Complex Nature of Dehydroepiandrosterone Sulfate Biosynthesis

Dehydroepiandrosterone (DHEA), a precursor of steroid sex hormones, is synthesized by steroid 17-alpha-hydroxylase/17,20-lyase (CYP17A1) with the participation of microsomal cytochrome b5 (CYB5A) and cytochrome P450 reductase (CPR), followed by sulfation by two cytosolic sulfotransferases, SULT1E1 and SULT2A1, for storage and transport to tissues in which its synthesis is not available. The involvement of CYP17A1 and SULTs in these successive reactions led us to consider the possible interaction of SULTs with DHEA-producing CYP17A1 and its redox partners. Text mining analysis, protein-protein network analysis, and gene co-expression analysis were performed to determine the relationships between SULTs and microsomal CYP isoforms. For the first time, using surface plasmon resonance, we detected interactions between CYP17A1 and SULT2A1 or SULT1E1. SULTs also interacted with CYB5A and CPR. The interaction parameters of SULT2A1/CYP17A1 and SULT2A1/CYB5A complexes seemed to be modulated by 3'-phosphoadenosine-5'-phosphosulfate (PAPS). Affinity purification, combined with mass spectrometry (AP-MS), allowed us to identify a spectrum of SULT1E1 potential protein partners, including CYB5A. We showed that the enzymatic activity of SULTs increased in the presence of only CYP17A1 or CYP17A1 and CYB5A mixture. The structures of CYP17A1/SULT1E1 and CYB5A/SULT1E1 complexes were predicted. Our data provide novel fundamental information about the organization of microsomal CYP-dependent macromolecular complexes.

Double Stranded DNA Binding Stapled Peptides: An Emerging Tool for Transcriptional Regulation

Stapled peptides have rapidly established themselves as a powerful technique to mimic α-helical interactions with a short peptide sequence. There are many examples of stapled peptides that successfully disrupt α-helix-mediated protein-protein interactions, with an example currently in clinical trials. DNA-protein interactions are also often mediated by α-helices and are involved in all transcriptional regulation processes. Unlike DNA-binding small molecules, which typically lack DNA sequence selectivity, DNA-binding proteins bind with high affinity and high selectivity. These are ideal candidates for the design DNA-binding stapled peptides. Despite the parallel to protein-protein interaction disrupting stapled peptides and the need for sequence specific DNA binders, there are very few DNA-binding stapled peptides. In this review we examine all the known DNA-binding stapled peptides. Their design concepts are compared to stapled peptides that disrupt protein-protein interactions and based on the few examples in the literature, DNA-binding stapled peptide trends are discussed.

Alchemical Calculation of Relative Free Energies for Charge-Changing Mutations at Protein-Protein Interfaces Considering Fixed and Variable Protonation States

The calculation of relative free energies (ΔΔG) for charge-changing mutations at protein-protein interfaces through alchemical methods remains challenging due to variations in the system's net charge during charging steps, the possibility of mutated and contacting ionizable residues occurring in various protonation states, and undersampling issues. In this study, we present a set of strategies, collectively termed TIRST/TIRST-H+, to address some of these challenges. Our approaches combine thermodynamic integration (TI) with the prediction of pKa shifts to calculate ΔΔG values. Moreover, special sets of restraints are employed to keep the alchemically transformed molecules separated. The accuracy of the devised approaches was assessed on a large and diverse data set comprising 164 point mutations of charged residues (Asp, Glu, Lys, and Arg) to Ala at the protein-protein interfaces of complexes with known three-dimensional structures. Mean absolute and root-mean-square errors ranging from 1.38 to 1.66 and 1.89 to 2.44 kcal/mol, respectively, and Pearson correlation coefficients of ∼0.6 were obtained when testing the approaches on the selected data set using the GPU-TI module of Amber18 suite and the ff14SB force field. Furthermore, the inclusion of variable protonation states for the mutated acid residues improved the accuracy of the predicted ΔΔG values. Therefore, our results validate the use of TIRST/TIRST-H+ in prospective studies aimed at evaluating the impact of charge-changing mutations to Ala on the stability of protein-protein complexes.

PICKLUSTER: a protein-interface clustering and analysis plug-in for UCSF ChimeraX

SUMMARY

The identification and characterization of interfaces in protein complexes is crucial for understanding the mechanisms of molecular recognition. These interfaces are also attractive targets for protein inhibition. However, targeting protein interfaces can be challenging for large interfaces that consist of multiple interacting regions. We present PICKLUSTER [Protein Interface C(K)luster]-a program for identifying "sub-interfaces" in protein-protein complexes using distance clustering. The division of the interface into smaller "sub-interfaces" offers a more focused approach for targeting protein-protein interfaces.

AVAILABILITY AND IMPLEMENTATION

PICKLUSTER is implemented as a plug-in for the molecular visualization program UCSF ChimeraX 1.4 and subsequent versions. It is freely available for download in the ChimeraX Toolshed and https://gitlab.com/topf-lab/pickluster.git.

Developing similarity matrices for antibody-protein binding interactions

The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces.

Antibody designing against IIIabc junction (JIIIabc) of HCV IRES through affinity maturation; RNA-Antibody docking and interaction analysis

Hepatitis C virus is a single-stranded RNA based virus which can cause chronic HCV and hepatocellular carcinoma. HCV genotype 3a has relatively higher rate of fibrosis progression, prevalence of steatosis and incidence of HCC. Despite HCVs variation in genomic sequence, the 5' untranslated region containing internal ribosome entry site (IRES) is highly conserved among all genotypes. It is responsible for translation and initiation of the viral protein. In present study, IRES was targeted by designing variants of reported antigen binding fragment (Fab) through affinity maturation approach. Affinity maturation strategy allowed the rational antibody designing with better biophysical properties and antibody-antigen binding interactions. Complementarity determining regions of reported Fab (wild type) were assessed and docked with IRES. Best generated model of Fab was selected and subjected to alanine scanning Three sets of insilico mutations for variants (V) designing were selected; single (1-71), double (a-j) and triple (I-X). Redocking of IRES-Fab variants consequently enabled the discovery of three variants exhibiting better docking score as compared to the wild type Fab. V1, V39 and V4 exhibited docking scores of -446.51, -446.52 and-446.29 kcal/mol respectively which is better as compared to the wild type Fab that exhibited the docking score of -351.23 kcal/mol. Variants exhibiting better docking score were screened for aggregation propensity by assessing the aggregation prone regions in Fab structure. Total A3D scores of wild type Fab, V1, V4 and V39 were predicted as -315.325, -312.727, -316.967 and -317.545 respectively. It is manifested that solubility of V4 and V39 is comparable to wild type Fab. In future, development and invitro assessment of these promising Fab HCV3 variants is aimed.

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

Electrostatic complementarity at the interface drives transient protein-protein interactions

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

How Single Amino Acid Substitutions Can Disrupt a Protein Hetero-Dimer Interface: Computational and Experimental Studies of the LigAB Dioxygenase from Sphingobium sp. Strain SYK-6

Protocatechuate 4,5-dioxygenase (LigAB) is a heterodimeric enzyme that catalyzes the dioxygenation of multiple lignin derived aromatic compounds. The active site of LigAB is at the heterodimeric interface, with specificity conferred by the alpha subunit and catalytic residues contributed by the beta subunit. Previous research has indicated that the phenylalanine at the 103 position of the alpha subunit (F103α) controls selectivity for the C5 position of the aromatic substrates, and mutations of this residue can enhance the rate of catalysis for substrates with larger functional groups at this position. While several of the mutations to this position (Valine, V; Threonine, T; Leucine, L; and Histidine, H) were catalytically active, other mutations (Alanine, A; and Serine, S) were found to have reduced dimer interface affinity, leading to challenges in copurifing the catalytically active enzyme complex under high salt conditions. In this study, we aimed to experimentally and computationally interrogate residues at the dimer interface to discern the importance of position 103α for maintaining the integrity of the heterodimer. Molecular dynamic simulations and electrophoretic mobility assays revealed a preference for nonpolar/aromatic amino acids in this position, suggesting that while substitutions to polar amino acids may produce a dioxygenase with a useful substrate utilization profile, those considerations may be off-set by potential destabilization of the catalytically active oligomer. Understanding the dimerization of LigAB provides insight into the multimeric proteins within the largely uncharacterized superfamily and characteristics to consider when engineering proteins that can degrade lignin efficiently. These results shed light on the challenges associated with engineering proteins for broader substrate specificity.

Molecular Peptide Grafting as a Tool to Create Novel Protein Therapeutics

The study of peptides (synthetic or corresponding to discrete regions of proteins) has facilitated the understanding of protein structure-activity relationships. Short peptides can also be used as powerful therapeutic agents. However, the functional activity of many short peptides is usually substantially lower than that of their parental proteins. This is (as a rule) due to their diminished structural organization, stability, and solubility often leading to an enhanced propensity for aggregation. Several approaches have emerged to overcome these limitations, which are aimed at imposing structural constraints into the backbone and/or sidechains of the therapeutic peptides (such as molecular stapling, peptide backbone circularization and molecular grafting), therefore enforcing their biologically active conformation and thus improving their solubility, stability, and functional activity. This review provides a short summary of approaches aimed at enhancing the biological activity of short functional peptides with a particular focus on the peptide grafting approach, whereby a functional peptide is inserted into a scaffold molecule. Intra-backbone insertions of short therapeutic peptides into scaffold proteins have been shown to enhance their activity and render them a more stable and biologically active conformation.

DockNet: high-throughput protein-protein interface contact prediction

MOTIVATION

Over 300 000 protein-protein interaction (PPI) pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting PPI sites, however, is a challenging task that traditionally requires computationally expensive and time-consuming docking simulations. A major weakness of modern protein docking algorithms is the inability to account for protein flexibility, which ultimately leads to relatively poor results.

RESULTS

Here, we propose DockNet, an efficient Siamese graph-based neural network method which predicts contact residues between two interacting proteins. Unlike other methods that only utilize a protein's surface or treat the protein structure as a rigid body, DockNet incorporates the entire protein structure and places no limits on protein flexibility during an interaction. Predictions are modeled at the residue level, based on a diverse set of input node features including residue type, surface accessibility, residue depth, secondary structure, pharmacophore and torsional angles. DockNet is comparable to current state-of-the-art methods, achieving an area under the curve (AUC) value of up to 0.84 on an independent test set (DB5), can be applied to a variety of different protein structures and can be utilized in situations where accurate unbound protein structures cannot be obtained.

AVAILABILITY AND IMPLEMENTATION

DockNet is available at https://github.com/npwilliams09/docknet and an easy-to-use webserver at https://biosig.lab.uq.edu.au/docknet. All other data underlying this article are available in the article and in its online supplementary material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cold spots are universal in protein-protein interactions

Proteins interact with each other through binding interfaces that differ greatly in size and physico-chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein-protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo- and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high-affinity complexes showing fewer centrally located colds spots than low-affinity complexes. A larger number of cold spots is also found in non-cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo-dimeric complexes compared to hetero-complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.

Evidence of an intracellular interaction between the Escherichia coli enzymes EntC and EntB and identification of a potential electrostatic channeling surface

Siderophores are high-affinity small-molecule chelators employed by bacteria to acquire iron from the extracellular environment. The Gram-negative bacterium Escherichia coli synthesizes and secretes enterobactin, a tris-catechol siderophore. Enterobactin is synthesized by six cytoplasmic enzyme activities: EntC, EntB (isochorismatase (IC) domain), EntA, EntE, EntB (aryl carrier protein (ArCP) domain), and EntF. While various pairwise protein-protein interactions have been reported between EntB, EntA, EntE, and EntF, evidence for an interaction between EntC and EntB has remained elusive. We have employed bacterial two-hybrid assays and in vivo crosslinking to demonstrate an intracellular EntC-EntB interaction. A T18-EntC/T25-EntB co-transformant exhibited a positive two-hybrid signal compared to a control T18-EntC/T25 co-transformant. In vivo formaldehyde crosslinking of E. coli cells co-expressing HA-tagged EntB and H6-tagged EntC resulted in an observable ∼80 kDa band on Western blots that cross-reacted with anti-HA and anti-H6, corresponding to one HA-EntB monomer (33 kDa) crosslinked with one H6-EntC monomer (45 kDa). This band disappeared upon sample boiling, confirming it to be a formaldehyde-crosslinked species. Bands of molecular masses greater than 80 kDa that cross-reacted with both antibodies were also observed. Automated docking of the crystal structures of monomeric EntC and dimeric EntB resulted in a top-ranked candidate docked ensemble in which the active sites of EntC and EntB were oriented in apposition and connected by an electropositive surface potentially capable of channeling negatively charged isochorismate. These research outcomes provide the first reported evidence of an EntC-EntB interaction, as well as the first experimental evidence of higher-order complexes containing EntC and EntB.

Targeting Undruggable Transcription Factors with PROTACs: Advances and Perspectives

Dysregulation of transcription factors has been implicated in a variety of human diseases. However, these proteins have traditionally been regarded as undruggable and only a handful of them have been successfully targeted by conventional small molecules. Moreover, the development of intrinsic and acquired resistance has hampered the clinical use of these agents. Over the past years, proteolysis-targeting chimeras (PROTACs) have shown great promise because of their potential for overcoming drug resistance and their ability to target previously undruggable proteins. Indeed, several small molecule-based PROTACs have demonstrated superior efficacy in therapy-resistant metastatic cancers. Nevertheless, it remains challenging to identify ligands for the majority of transcription factors. Given that transcription factors recognize short DNA motifs in a sequence-specific manner, multiple novel approaches exploit DNA motifs as warheads in PROTAC design for the degradation of aberrant transcription factors. These PROTACs pave the way for targeting undruggable transcription factors with potential therapeutic benefits.

A unified statistical potential reveals that amino acid stickiness governs nonspecific recruitment of client proteins into condensates

Membraneless organelles are cellular compartments that form by liquid-liquid phase separation of one or more components. Other molecules, such as proteins and nucleic acids, will distribute between the cytoplasm and the liquid compartment in accordance with the thermodynamic drive to lower the free energy of the system. The resulting distribution colocalizes molecular species to carry out a diversity of functions. Two factors could drive this partitioning: the difference in solvation between the dilute versus dense phase and intermolecular interactions between the client and scaffold proteins. Here, we develop a set of knowledge-based potentials that allow for the direct comparison between stickiness, which is dominated by desolvation energy, and pairwise residue contact propensity terms. We use these scales to examine experimental data from two systems: protein cargo dissolving within phase-separated droplets made from FG repeat proteins of the nuclear pore complex and client proteins dissolving within phase-separated FUS droplets. These analyses reveal a close agreement between the stickiness of the client proteins and the experimentally determined values of the partition coefficients (R > 0.9), while pairwise residue contact propensities between client and scaffold show weaker correlations. Hence, the stickiness of client proteins is sufficient to explain their differential partitioning within these two phase-separated systems without taking into account the composition of the condensate. This result implies that selective trafficking of client proteins to distinct membraneless organelles requires recognition elements beyond the client sequence composition. STATEMENT: Empirical potentials for amino acid stickiness and pairwise residue contact propensities are derived. These scales are unique in that they enable direct comparison of desolvation versus contact terms. We find that partitioning of a client protein to a condensate is best explained by amino acid stickiness.

Leveraging structural and 2D-QSAR to investigate the role of functional group substitutions, conserved surface residues and desolvation in triggering the small molecule-induced dimerization of hPD-L1

Small molecules are rising as a new generation of immune checkpoints’ inhibitors, with compounds targeting the human Programmed death-ligand 1 (hPD-L1) protein are pioneering this area of research. Promising examples include the recently disclosed compounds from Bristol-Myers-Squibb (BMS). These molecules bind specifically to hPD-L1 through a unique mode of action. They induce dimerization between two hPD-L1 monomers through the hPD-1 binding interface in each monomer, thereby inhibiting the PD-1/PD-L1 axis. While the recently reported crystal structures of such small molecules bound to hPD-L1 reveal valuable insights regarding their molecular interactions, there is still limited information about the dynamics driving this unusual complex formation. The current study provides an in-depth computational structural analysis to study the interactions of five small molecule compounds in complex with hPD-L1. By employing a combination of molecular dynamic simulations, binding energy calculations and computational solvent mapping techniques, our analyses quantified the dynamic roles of different hydrophilic and lipophilic residues at the surface of hPD-L1 in mediating these interactions. Furthermore, ligand-based analyses, including Free-Wilson 2D-QSAR was conducted to quantify the impact of R-group substitutions at different sites of the phenoxy-methyl biphenyl core. Our results emphasize the importance of a terminal phenyl ring that must be present in any hPD-L1 small molecule inhibitor. This phenyl moiety overlaps with a very unfavorable hydration site, which can explain the ability of such small molecules to trigger hPD-L1 dimerization.

Multi-task learning to leverage partially annotated data for PPI interface prediction

Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem. In addition, structure-based functional annotations, such as the PPI interface annotations, are scarce: only for about one-third of all protein structures residue-based PPI interface annotations are available. If we want to use a deep learning strategy, we have to overcome the problem of limited data availability. Here we use a multi-task learning strategy that can handle missing data. We start with the multi-task model architecture, and adapted it to carefully handle missing data in the cost function. As related learning tasks we include prediction of secondary structure, solvent accessibility, and buried residue. Our results show that the multi-task learning strategy significantly outperforms single task approaches. Moreover, only the multi-task strategy is able to effectively learn over a dataset extended with structural feature data, without additional PPI annotations. The multi-task setup becomes even more important, if the fraction of PPI annotations becomes very small: the multi-task learner trained on only one-eighth of the PPI annotations—with data extension—reaches the same performances as the single-task learner on all PPI annotations. Thus, we show that the multi-task learning strategy can be beneficial for a small training dataset where the protein’s functional properties of interest are only partially annotated.

Bio-informatic analysis of CRISPR protospacer adjacent motifs (PAMs) in T4 genome

BACKGROUND

The existence of protospacer adjacent motifs (PAMs) sequences in bacteriophage genome is critical for the recognition and function of the clustered regularly interspaced short palindromic repeats-Cas (CRISPR-Cas) machinery system. We further elucidate the significance of PAMs and their function, particularly as a part of transcriptional regulatory regions in T4 bacteriophages.

METHODS

A scripting language was used to analyze a sequence of T4 phage genome, and a list of few selected PAMs. Mann-Whitney Wilcoxon (MWW) test was used to compare the sequence hits for the PAMs versus the hits of all the possible sequences of equal lengths.

RESULTS

The results of MWW test show that certain PAMs such as: 'NGG' and 'TATA' are preferably located at the core of phage promoters: around -10 position, whereas the position around -35 appears to have no detectable count variation of any of the tested PAMs. Among all tested PAMs, the following three sequences: 5'-GCTV-3', 5'-TTGAAT-3' and 5'-TTGGGT-3' have higher prevalence in essential genes. By analyzing all the possible ways of reading PAM sequences as codons for the corresponding amino acids, it was found that deduced amino acids of some PAMs have a significant tendency to prefer the surface of proteins.

CONCLUSION

These results provide novel insights into the location and the subsequent identification of the role of PAMs as transcriptional regulatory elements. Also, CRISPR targeting certain PAM sequences is somehow likely to be connected to the hydrophilicity (water solubility) of amino acids translated from PAM's triplets. Therefore, these amino acids are found at the interacting unit at protein-protein interfaces.

Fever as an evolutionary agent to select immune complexes interfaces

We herein analyzed all available protein–protein interfaces of the immune complexes from the Protein Data Bank whose antigens belong to pathogens or cancers that are modulated by fever in mammalian hosts. We also included, for comparison, protein interfaces from immune complexes that are not significantly modulated by the fever response. We highlight the distribution of amino acids at these viral, bacterial, protozoan and cancer epitopes, and at their corresponding paratopes that belong strictly to monoclonal antibodies. We identify the “hotspots”, i.e. residues that are highly connected at such interfaces, and assess the structural, kinetic and thermodynamic parameters responsible for complex formation. We argue for an evolutionary pressure for the types of residues at these protein interfaces that may explain the role of fever as a selective force for optimizing antibody binding to antigens.

Y380Q novel mutation in receptor-binding domain of SARS-CoV-2 spike protein together with C379W interfere in the neutralizing antibodies interaction

We aimed to describe the SARS-CoV-2 lineages circulating early pandemic among samples with S gene dropout and characterize the receptor-binding domain (RBD) of viral spike protein. Adults and children older than 2 months with signs and symptoms of COVID-19 were prospectively enrolled from May to October in Porto Alegre, Brazil. All participants performed RT-PCR assay, and samples with S gene dropout and cycle threshold < 30 were submitted to high-throughput sequencing (HTS). 484 out of 1,557 participants tested positive for SARS-CoV-2. The S gene dropout was detected in 7.4% (36/484) and a peak was observed in August. The B.1.1.28, B.1.91 and B.1.1.33 lineages were circulating in early pandemic. The RBD novel mutation (Y380Q) was found in one sample occurring simultaneously with C379W and V395A, and the B.1.91 lineage in the spike protein. The Y380Q and C379W may interfere with the binding of neutralizing antibodies (CR3022, EY6A, H014, S304).

Protein-protein interactions enhance the thermal resilience of SpyRing-cyclized enzymes: A molecular dynamic simulation study

Recently a technique based on the interaction between adhesion proteins extracted from Streptococcus pyogenes, known as SpyRing, has been widely used to improve the thermal resilience of enzymes, the assembly of biostructures, cancer cell recognition and other fields. It was believed that the covalent cyclization of protein skeleton caused by SpyRing reduces the conformational entropy of biological structure and improves its rigidity, thus improving the thermal resilience of the target enzyme. However, the effects of SpyTag/ SpyCatcher interaction with this enzyme are poorly understood, and their regulation of enzyme properties remains unclear. Here, for simplicity, we took the single domain enzyme lichenase from Bacillus subtilis 168 as an example, studied the interface interactions in the SpyRing by molecular dynamics simulations, and examined the effects of the changes of electrostatic interaction and van der Waals interaction on the thermal resilience of target enzyme. The simulations showed that the interface between SpyTag/SpyCatcher and the target enzyme is different from that found by geometric matching method and highlighted key mutations at the interface that might have effect on the thermal resilience of the enzyme. Our calculations highlighted interfacial interactions between enzyme and SpyTag/SpyCatcher, which might be useful in rational designs of the SpyRing.

Probing GPCR Dimerization Using Peptides

G protein-coupled receptors (GPCRs) are the largest class of membrane proteins and the most common and extensively studied pharmacological target. Numerous studies over the last decade have confirmed that GPCRs do not only exist and function in their monomeric form but in fact, have the ability to form dimers or higher order oligomers with other GPCRs, as well as other classes of receptors. GPCR oligomers have become increasingly attractive to investigate as they have the ability to modulate the pharmacological responses of the receptors which in turn, could have important functional roles in diseases, such as cancer and several neurological & neuropsychiatric disorders. Despite the growing evidence in the field of GPCR oligomerisation, the lack of structural information, as well as targeting the 'undruggable' protein-protein interactions (PPIs) involved in these complexes, has presented difficulties. Outside the field of GPCRs, targeting PPIs has been widely studied, with a variety of techniques being investigated; from small-molecule inhibitors to disrupting peptides. In this review, we will demonstrate several physiologically relevant GPCR dimers and discuss an array of strategies and techniques that can be employed when targeting these complexes, as well as provide ideas for future development.

Grafting Hydrophobic Amino Acids Critical for Inhibition of Protein-Protein Interactions on a Cell-Penetrating Peptide Scaffold

Stapled peptides are a promising class of conformationally restricted peptides for modulating protein-protein interactions (PPIs). However, the low membrane permeability of these peptides is an obstacle to their therapeutic applications. It is common that only a few hydrophobic amino acid residues are mandatory for stapled peptides to bind to their target proteins. Hoping to create a novel class of membrane-permeable PPI inhibitors, the phenylalanine, tryptophan, and leucine residues that play a critical role in inhibiting the p53-HDM2 interaction were grafted into the framework of CADY2─a cell-penetrating peptide (CPP) having a helical propensity. Two analogues (CADY-3FWL and CADY-10FWL) induced apoptotic cell death but lacked the intended HDM2 interaction. Pull-down experiments followed by proteomic analysis led to the elucidation of nesprin-2 as a candidate binding target. Nesprin-2 is considered to play a role in the nuclear translocation of β-catenin upon activation of the Wnt signaling pathway, which leads to the expression of antiapoptosis proteins and cell survival. Cells treated with the two analogues showed decreased nuclear localization of β-catenin and reduced mRNA expression of related antiapoptotic proteins. These data suggest inhibition of β-catenin nuclear translocation as a possible mode of action of the described cell-penetrating stapled peptides.

Comprehensive exploration of chemical space using trisubstituted carboranes

A total of 42 trisubstituted carboranes categorised into five scaffolds were systematically designed and synthesized by exploiting the different reactivities of the twelve vertices of o-, m-, and p-carboranes to cover all directions in chemical space. Significant inhibitors of hypoxia inducible factor transcriptional activitay were mainly observed among scaffold V compounds (e.g., Vi–m, and Vo), whereas anti-rabies virus activity was observed among scaffold V (Va–h), scaffold II (IIb–g), and scaffold IV (IVb) compounds. The pharmacophore model predicted from compounds with scaffold V, which exhibited significant anti-rabies virus activity, agreed well with compounds IIb–g with scaffold II and compound IVb with scaffold IV. Normalized principal moment of inertia analysis indicated that carboranes with scaffolds I–V cover all regions in the chemical space. Furthermore, the first compounds shown to stimulate the proliferation of the rabies virus were found among scaffold V carboranes.

Enhanced Molecular Dynamics Method to Efficiently Increase the Discrimination Capability of Computational Protein-Protein Docking

Protein–protein docking typically consists of the generation of putative binding conformations, which are subsequently ranked by fast heuristic scoring functions. The simplicity of these functions allows for computational efficiency but has severe repercussions on their discrimination capabilities. In this work, we show the effectiveness of suitable descriptors calculated along short scaled molecular dynamics runs in recognizing the nearest-native bound conformation among a set of putative structures generated by the HADDOCK tool for eight protein–protein systems.

Rational Design of Constrained Peptides as Protein Interface Inhibitors

The lack of progress in developing targeted therapeutics directed at protein–protein complexes has been due to the absence of well-defined ligand-binding pockets and the extensive intermolecular contacts at the protein–protein interface. Our laboratory has developed approaches to dissect protein–protein complexes focusing on the superfamilies of erbB and tumor necrosis factor (TNF) receptors by the combined use of structural biology and computational biology to facilitate small molecule development. We present a perspective on the development and application of peptide inhibitors as well as immunoadhesins to cell surface receptors performed in our laboratory.

Protein-protein interactions at a glance: Protocols for the visualization of biomolecular interactions

Protein-protein interactions (PPIs) play a key role in many biological processes and are intriguing targets for drug discovery campaigns. Advancements in experimental and computational techniques are leading to a growth of data accessibility, and, with it, an increased need for the analysis of PPIs. In this respect, visualization tools are essential instruments to represent and analyze biomolecular interactions. In this chapter, we reviewed some of the available tools, highlighting their features, and describing their functions with practical information on their usage.