Principles of Protein−Protein Interactions: What are the Preferred Ways For Proteins To Interact?

Identification of tomato F-box proteins functioning in phenylpropanoid metabolism

Phenylpropanoids, a class of specialized metabolites, play crucial roles in plant growth and stress adaptation and include diverse phenolic compounds such as flavonoids. Phenylalanine ammonia-lyase (PAL) and chalcone synthase (CHS) are essential enzymes functioning at the entry points of general phenylpropanoid biosynthesis and flavonoid biosynthesis, respectively. In Arabidopsis, PAL and CHS are turned over through ubiquitination-dependent proteasomal degradation. Specific kelch domain-containing F-Box (KFB) proteins as components of ubiquitin E3 ligase directly interact with PAL or CHS, leading to polyubiquitinated PAL and CHS, which in turn influences phenylpropanoid and flavonoid production. Although phenylpropanoids are vital for tomato nutritional value and stress responses, the post-translational regulation of PAL and CHS in tomato remains unknown. We identified 31 putative KFB-encoding genes in the tomato genome. Our homology analysis and phylogenetic study predicted four PAL-interacting SlKFBs, while SlKFB18 was identified as the sole candidate for the CHS-interacting KFB. Consistent with their homolog function, the predicted four PAL-interacting SlKFBs function in PAL degradation. Surprisingly, SlKFB18 did not interact with tomato CHS and the overexpression or knocking out of SlKFB18 did not affect phenylpropanoid contents in tomato transgenic lines, suggesting its irreverence with flavonoid metabolism. Our study successfully discovered the post-translational regulatory machinery of PALs in tomato while highlighting the limitation of relying solely on a homology-based approach to predict interacting partners of F-box proteins.

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

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

Molecular glues and induced proximity: An evolution of tools and discovery

Small molecule molecular glues can nucleate protein complexes and rewire interactomes. Molecular glues are widely used as probes for understanding functional proximity at a systems level, and the potential to instigate event-driven pharmacology has motivated their application as therapeutics. Despite advantages such as cell permeability and the potential for low off-target activity, glues are still rare when compared to canonical inhibitors in therapeutic development. Their often simple structure and specific ability to reshape protein-protein interactions pose several challenges for widespread, designer applications. Molecular glue discovery and design campaigns can find inspiration from the fields of synthetic biology and biophysics to mine chemical libraries for glue-like molecules.

A Transdermal Prion-Bionics Supermolecule as a RAB3A Antagonist for Enhancing Facial Youthfulness

The mechanism research of skin wrinkles, conducted on volunteers underwent high-intensity desk work and mice subjected to partial sleep deprivation, revealed a significant reduction in dermal thickness associated with the presence of wrinkles. This can be attributed to the activation of facial nerves in a state of hysteria due to an abnormally elevated interaction between SNAP25 and RAB3A proteins involved in the synaptic vesicle cycle (SVC). Facilitated by AI-assisted structural design, a refined peptide called RSIpep is developed to modulate this interaction and normalize SVC. Drawing inspiration from prions, which possess the ability to protect themselves against proteolysis and invade neighboring nerve cells through macropinocytosis, RSIpep is engineered to demonstrate a GSH-responsive reversible self-assembly into a prion-like supermolecule (RSIprion). RSIprion showcases protease resistance, micropinocytosis-dependent cellular internalization, and low adhesion with constituent molecules in the cuticle, thereby endowing it with the transdermic absorption and subsequent biofunction in redressing the frenzied SVC. As a facial mud mask, it effectively reduces periorbital and perinasal wrinkles in the human face. Collectively, RSIprion not only presents a clinical potential as an anti-wrinkle prion-like supermolecule, but also exemplifies a reproducible instance of bionic strategy-guided drug development that bestows transdermal ability upon the pharmaceutical molecule.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Folding and Binding Kinetics of the Tandem of SH2 Domains from SHP2

The SH2 domains of SHP2 play a crucial role in determining the function of the SHP2 protein. While the folding and binding properties of the isolated NSH2 and CSH2 domains have been extensively studied, there is limited information about the tandem SH2 domains. This study aims to elucidate the folding and binding kinetics of the NSH2-CSH2 tandem domains of SHP2 through rapid kinetic experiments, complementing existing data on the isolated domains. The results indicate that while the domains generally fold and unfold independently, acidic pH conditions induce complex scenarios involving the formation of a misfolded intermediate. Furthermore, a comparison of the binding kinetics of isolated NSH2 and CSH2 domains with the NSH2-CSH2 tandem domains, using peptides that mimic specific portions of Gab2, suggests a dynamic interplay between NSH2 and CSH2 in binding Gab2 that modulate the microscopic association rate constant of the binding reaction. These findings, discussed in the context of previous research on the NSH2 and CSH2 domains, enhance our understanding of the function of the SH2 domain tandem of SHP2.

MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism

Proteins play a vital role in various biological processes and achieve their functions through protein-protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://github.com/YyinGong/MR2CPPIS.

An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction

In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulations. While the gains in AUC and other metrics might seem modest, they contribute to a model that is more robust, consistent, and adaptable. To assess the effectiveness of various approaches, we compared the performance of logistic regression to four baseline models. Our results indicate that logistic regression consistently underperforms across all evaluated metrics. This suggests that it may not be well-suited to capture the complex relationships within this dataset. Tree-based models, on the other hand, appear to be more effective for problems involving molecular dynamics simulations. Extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) are optimized for performance and speed, handling datasets effectively and incorporating regularizations to avoid over-fitting. Our findings indicate that the ensemble method enhances the predictive capability of PPIs, offering a promising tool for computational biology and drug discovery by accurately identifying potential interaction sites and facilitating the understanding of complex protein functions within biological systems.

Cell-penetrating protein-recognizing polymeric nanoparticles through dynamic covalent chemistry and double imprinting

Molecular recognition of proteins is key to their biological functions and processes such as protein-protein interactions (PPIs). The large binding interface involved and an often relatively flat binding surface make the development of selective protein-binding materials extremely challenging. A general method is reported in this work to construct protein-binding polymeric nanoparticles from cross-linked surfactant micelles. Preparation involves first dynamic covalent chemistry that encodes signature surface lysines on a protein template. A double molecular imprinting procedure fixes the binding groups on the nanoparticle for these lysine groups, meanwhile creating a binding interface complementary to the protein in size, shape, and distribution of acidic groups on the surface. These water-soluble nanoparticles possess excellent specificities for target proteins and sufficient affinities to inhibit natural PPIs such as those between cytochrome c (Cytc) and cytochrome c oxidase (CcO). With the ability to enter cells through a combination of energy-dependent and -independent pathways, they intervene apoptosis by inhibiting the PPI between Cytc and the apoptotic protease activating factor-1 (APAF1). Generality of the preparation and the excellent molecular recognition of the materials have the potential to make them powerful tools to probe protein functions in vitro and in cellulo.

ProInterVal: Validation of Protein-Protein Interfaces through Learned Interface Representations

Proteins are vital components of the biological world and serve a multitude of functions. They interact with other molecules through their interfaces and participate in crucial cellular processes. Disruption of these interactions can have negative effects on organisms, highlighting the importance of studying protein-protein interfaces for developing targeted therapies for diseases. Therefore, the development of a reliable method for investigating protein-protein interactions is of paramount importance. In this work, we present an approach for validating protein-protein interfaces using learned interface representations. The approach involves using a graph-based contrastive autoencoder architecture and a transformer to learn representations of protein-protein interaction interfaces from unlabeled data and then validating them through learned representations with a graph neural network. Our method achieves an accuracy of 0.91 for the test set, outperforming existing GNN-based methods. We demonstrate the effectiveness of our approach on a benchmark data set and show that it provides a promising solution for validating protein-protein interfaces.

Screening 2D Materials for Their Nanotoxicity toward Nucleic Acids and Proteins: An In Silico Outlook

Since the discovery of graphene, two-dimensional (2D) materials have been anticipated to demonstrate enormous potential in bionanomedicine. Unfortunately, the majority of 2D materials induce nanotoxicity via disruption of the structure of biomolecules. Consequently, there has been an urge to synthesize and identify biocompatible 2D materials. Before the cytotoxicity of 2D nanomaterials is experimentally tested, computational studies can rapidly screen them. Additionally, computational analyses can provide invaluable insights into molecular-level interactions. Recently, various "in silico" techniques have identified these interactions and helped to develop a comprehensive understanding of nanotoxicity of 2D materials. In this article, we discuss the key recent advances in the application of computational methods for the screening of 2D materials for their nanotoxicity toward two important categories of abundant biomolecules, namely, nucleic acids and proteins. We believe the present article would help to develop newer computational protocols for the identification of novel biocompatible materials, thereby paving the way for next-generation biomedical and therapeutic applications based on 2D materials.

Recent Progress in Site-Selective Modification of Peptides and Proteins Using Macrocycles

Peptides and proteins undergo crucial modifications to alter their physicochemical properties to expand their applications in diverse fields. Various techniques, such as unnatural amino acid incorporation, enzyme catalysis, and chemoselective methods, have been employed for site-selective peptide and protein modification. While traditional methods remain valuable, advancement in host-guest chemistry introduces innovative and promising approaches for the selective modification of peptides and proteins. Macrocycles exhibit robust binding affinities, particularly with natural amino acids, which facilitates their use in selectively binding to specific sequences. This distinctive property endows macrocycles with the potential for modification of target peptides and proteins. This review provides a comprehensive overview of strategies utilizing macrocycles for the selective modification of peptides and proteins. These strategies unlock new possibilities for constructing antibody-drug conjugates and stabilizing volatile medications.

Molecular basis for curvature formation in SepF polymerization

The self-assembly of proteins into curved structures plays an important role in many cellular processes. One good example of this phenomenon is observed in the septum-forming protein (SepF), which forms polymerized structures with uniform curvatures. SepF is essential for regulating the thickness of the septum during bacteria cell division. In Bacillus subtilis, SepF polymerization involves two distinct interfaces, the β-β and α-α interfaces, which define the assembly unit and contact interfaces, respectively. However, the mechanism of curvature formation in this step is not yet fully understood. In this study, we employed solid-state NMR (SSNMR) to compare the structures of cyclic wild-type SepF assemblies with linear assemblies resulting from a mutation of G137 on the β-β interface. Our results demonstrate that while the sequence differences arise from the internal assembly unit, the dramatic changes in the shape of the assemblies depend on the α-α interface between the units. We further provide atomic-level insights into how the angular variation of the α2 helix on the α-α interface affects the curvature of the assemblies, using a combination of SSNMR, cryo-electron microscopy, and simulation methods. Our findings shed light on the shape control of protein assemblies and emphasize the importance of interhelical contacts in retaining curvature.

De novo design of a protein binder against Staphylococcus enterotoxin B

Staphylococcus enterotoxin B (SEB) interacts with MHC-II molecules to overactivate immune cells and thereby to produce excessive pro-inflammatory cytokines. Disrupting the interactions between SEB and MHC-II helps eliminate the lethal threat posed by SEB. In this study, a de novo computational approach was used to design protein binders targeting SEB. The MHC-II binding domain of SEB was selected as the target, and the possible promising binding mode was broadly explored. The obtained original binder was folded into triple-helix bundles and contained 56 amino acids with molecular weight 5.9 kDa. The interface of SEB and the binder was highly hydrophobic. ProteinMPNN optimization further enlarged the hydrophobic region of the binder and improved the stability of the binder-SEB complex. In vitro study demonstrated that the optimized binder significantly inhibited the inflammatory response induced by SEB. Overall, our research demonstrated the applicability of this approach in de novo designing protein binders against SEB, and thereby providing potential therapeutics for SEB induced diseases.

Contribution of Signaling Partner Association to Strigolactone Receptor Selectivity

The parasitic plant witchweed, Striga hermonthica, results in agricultural losses of billions of dollars per year. It perceives its host via plant hormones called strigolactones, which act as germination stimulants for witchweed. Strigolactone signaling involves substrate binding to the strigolactone receptor, followed by substrate hydrolysis and a conformational change from an inactive, or open state, to an active, or closed state. In the active state, the receptor associates with a signaling partner, MAX2. Recently, it was shown that this MAX2 association process acts as a strong contributor to the uniquely high signaling activity observed in ShHTL7; however, it is unknown why ShHTL7 has enhanced MAX2 association affinity. Using an umbrella sampling molecular dynamics approach, we characterized the association processes of AtD14, ShHTL7, a mutant of ShHTL7, and ShHTL6 with MAX2 homologue OsD3. From these results, we show that ShHTL7 has an enhanced standard binding free energy of OsD3 compared to those of the other receptors. Additionally, our results suggest that the overall topology of the T2/T3 helix region is likely an important modulator of MAX2 binding. Thus, differences in MAX2 association, modulated by differences in the T2/T3 helix region, are a contributor to differences in signaling activity between different strigolactone receptors.

Delivery of external proteins into the cytoplasm using protein capsules modified with IgG on the surface, created from the amphiphilic two helix-bundle protein OLE-ZIP

This study explores a new method for delivering therapeutic proteins into specific cells using OLE-ZIP capsules that present IgG. OLE-ZIP capsules is a spherical caspules prepared from amphihilic dimetic coiled-coil peptide, OLE-ZIP. Upon presenting cetuximab, these capsules showed preferential uptake in A431 cells and increased cytotoxicity when loaded with RNase A.

ASD2023: towards the integrating landscapes of allosteric knowledgebase

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

Sampling and Scoring in Protein-Protein Docking

Protein-protein docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.

Hot-Spot Residue-Based Virtual Screening of Novel Selective Estrogen-Receptor Degraders for Breast Cancer Treatment

The estrogen-receptor alfa (ERα) is considered pivotal for breast cancer treatment. Although selective estrogen-receptor degraders (SERDs) have been developed to induce ERα degradation and antagonism, their agonistic effect on the uterine tissue and poor pharmacokinetic properties limit further application of ERα; thus, discovering novel SERDs is necessary. The ligand preferentially interacts with several key residues of the protein (defined as hot-spot residues). Improving the interaction with hot-spot residues of ERα offers a promising avenue for obtaining novel SERDs. In this study, pharmacophore modeling, molecular mechanics/generalized Born surface area (MM/GBSA), and amino-acid mutation were combined to determine several hot-spot residues. Focusing on the interaction with these hot-spot residues, hit fragments A1-A3 and A9 were virtually screened from two fragment libraries. Finally, these hit fragments were linked to generate compounds B1-B3, and their biological activities were evaluated. Remarkably, compound B1 exhibited potent antitumor activity against MCF-7 cells (IC50 = 4.21 nM), favorable ERα binding affinity (Ki = 14.6 nM), and excellent ERα degradative ability (DC50 = 9.7 nM), which indicated its potential to evolve as a promising SERD for breast cancer treatment.

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

Mce1R of Mycobacterium tuberculosis prefers long-chain fatty acids as specific ligands: a computational study

The mce1 operon of Mycobacterium tuberculosis, which codes the Mce1 transporter, facilitates the transport of fatty acids. Fatty acids are one of the major sources for carbon and energy for the pathogen during its intracellular survival and pathogenicity. The mce1 operon is transcriptionally regulated by Mce1R, a VanR-type regulator, which could bind specific ligands and control the expression of the mce1 operon accordingly. This work reports computational identification of Mce1R-specific ligands. Initially by employing cavity similarity search algorithm by the ProBis server, the cavities of the proteins similar to that of Mce1R and the bound ligands were identified from which fatty acids were selected as the potential ligands. From the earlier-generated monomeric structure, the dimeric structure of Mce1R was then modeled by the GalaxyHomomer server and validated computationally to use in molecular docking and molecular dynamics simulation analysis. The fatty acid ligands were found to dock within the cavity of Mce1R and the docked complexes were subjected to molecular dynamics simulation to explore their stabilities and other dynamic properties. The data suggest that Mce1R preferably binds to long-chain fatty acids and undergoes distinct structural changes upon binding.

Characterization of Two-Component System gene (TCS) in melatonin-treated common bean under salt and drought stress

The two-component system (TCS) generally consists of three elements, namely the histidine kinase (HK), response regulator (RR), and histidine phosphotransfer (HP) gene families. This study aimed to assess the expression of TCS genes in P. vulgaris leaf tissue under salt and drought stress and perform a genome-wide analysis of TCS gene family members using bioinformatics methods. This study identified 67 PvTCS genes, including 10 PvHP, 38 PvRR, and 19 PvHK, in the bean genome. PvHK2 had the maximum number of amino acids with 1261, whilst PvHP8 had the lowest number with 87. In addition, their theoretical isoelectric points were between 4.56 (PvHP8) and 9.15 (PvPRR10). The majority of PvTCS genes are unstable. Phylogenetic analysis of TCS genes in A. thaliana, G. max, and bean found that PvTCS genes had close phylogenetic relationships with the genes of other plants. Segmental and tandem duplicate gene pairs were detected among the TCS genes and TCS genes have been subjected to purifying selection pressure in the evolutionary process. Furthermore, the TCS gene family, which has an important role in abiotic stress and hormonal responses in plants, was characterized for the first time in beans, and its expression of TCS genes in bean leaves under salt and drought stress was established using RNAseq and qRT-PCR analyses. The findings of this study will aid future functional and genomic studies by providing essential information about the members of the TCS gene family in beans.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01406-5.

Gibbs Energy and Gene Expression Combined as a New Technique for Selecting Drug Targets for Inhibiting Specific Protein-Protein Interactions

One of the most important aspects of successful cancer therapy is the identification of a target protein for inhibition interaction. Conventionally, this consists of screening a panel of genes to assess which is mutated and then developing a small molecule to inhibit the interaction of two proteins or to simply inhibit a specific protein from all interactions. In previous work, we have proposed computational methods that analyze protein-protein networks using both topological approaches and thermodynamic quantification provided by Gibbs free energy. In order to make these approaches both easier to implement and free of arbitrary topological filtration criteria, in the present paper, we propose a modification of the topological-thermodynamic analysis, which focuses on the selection of the most thermodynamically stable proteins and their subnetwork interaction partners with the highest expression levels. We illustrate the implementation of the new approach with two specific cases, glioblastoma (glioma brain tumors) and chronic lymphatic leukoma (CLL), based on the publicly available patient-derived datasets. We also discuss how this can be used in clinical practice in connection with the availability of approved and investigational drugs.

Protein-protein interaction and site prediction using transfer learning

The advanced language models have enabled us to recognize protein-protein interactions (PPIs) and interaction sites using protein sequences or structures. Here, we trained the MindSpore ProteinBERT (MP-BERT) model, a Bidirectional Encoder Representation from Transformers, using protein pairs as inputs, making it suitable for identifying PPIs and their respective interaction sites. The pretrained model (MP-BERT) was fine-tuned as MPB-PPI (MP-BERT on PPI) and demonstrated its superiority over the state-of-the-art models on diverse benchmark datasets for predicting PPIs. Moreover, the model's capability to recognize PPIs among various organisms was evaluated on multiple organisms. An amalgamated organism model was designed, exhibiting a high level of generalization across the majority of organisms and attaining an accuracy of 92.65%. The model was also customized to predict interaction site propensity by fine-tuning it with PPI site data as MPB-PPISP. Our method facilitates the prediction of both PPIs and their interaction sites, thereby illustrating the potency of transfer learning in dealing with the protein pair task.

How do the mutations in PfK13 protein promote anti-malarial drug resistance?

Plasmodium falciparum develops resistance to artemisinin upon exposure to the anti-malarial drug. Various mutations in the Plasmodium falciparum Kelch13 (PfK13) protein such as Y493H, R539T, I543T and C580Y have been associated with anti-malarial drug resistance. These mutations impede the regular ubiquitination process that eventually invokes drug resistance. However, the relationship between the mutation and the mechanism of drug resistance has not yet been fully elucidated. The comparative protein dynamics are studied by performing the classical molecular dynamics (MD) simulations and subsequent analysis of the trajectories adopting root-mean-square fluctuations, the secondary-structure predictions and the dynamical cross-correlation matrix analysis tools. Here, we observed that the mutations in the Kelch-domain do not have any structural impact on the mutated site; however, it significantly alters the overall dynamics of the protein. The loop-region of the BTB-domain especially for Y493H and C580Y mutants is found to have the enhanced dynamical fluctuations. The enhanced fluctuations in the BTB-domain could affect the protein-protein (PfK13-Cullin) binding interactions in the ubiquitination process and eventually lead to anti-malarial drug resistance.Communicated by Ramaswamy H. Sarma.

E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction

Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures. Recent studies have demonstrated benefits of employing graph convolution for PPI site prediction, but ignore symmetries naturally occurring in 3-dimensional space and act only on experimental coordinates. Here we present EquiPPIS, an E(3) equivariant graph neural network approach for PPI site prediction. EquiPPIS employs symmetry-aware graph convolutions that transform equivariantly with translation, rotation, and reflection in 3D space, providing richer representations for molecular data compared to invariant convolutions. EquiPPIS substantially outperforms state-of-the-art approaches based on the same experimental input, and exhibits remarkable robustness by attaining better accuracy with predicted structural models from AlphaFold2 than what existing methods can achieve even with experimental structures. Freely available at https://github.com/Bhattacharya-Lab/EquiPPIS, EquiPPIS enables accurate PPI site prediction at scale.

In vivo Protein Footprinting Reveals the Dynamic Conformational Changes of Proteome of Multiple Tissues in Progressing Alzheimer's Disease

Numerous studies have investigated changes in protein expression at the system level using proteomic mass spectrometry, but only recently have studies explored the structure of proteins at the proteome level. We developed covalent protein painting (CPP), a protein footprinting method that quantitatively labels exposed lysine, and have now extended the method to whole intact animals to measure surface accessibility as a surrogate of in vivo protein conformations. We investigated how protein structure and protein expression change as Alzheimer's disease (AD) progresses by conducting in vivo whole animal labeling of AD mice. This allowed us to analyze broadly protein accessibility in various organs over the course of AD. We observed that structural changes of proteins related to 'energy generation,' 'carbon metabolism,' and 'metal ion homeostasis' preceded expression changes in the brain. We found that proteins in certain pathways undergoing structural changes were significantly co-regulated in the brain, kidney, muscle, and spleen.

A review on structural mechanisms of protein-persistent organic pollutant (POP) interactions

With the advent of the industrial revolution, the accumulation of persistent organic pollutants (POPs) in the environment has become ubiquitous. POPs are halogen-containing organic molecules that accumulate, and remain in the environment for a long time, thus causing toxic effects in living organisms. POPs exhibit a high affinity towards biological macromolecules such as nucleic acids, proteins and lipids, causing genotoxicity and impairment of homeostasis in living organisms. Proteins are essential members of the biological assembly, as they stipulate all necessary processes for the survival of an organism. Owing to their stereochemical features, POPs and their metabolites form energetically favourable complexes with proteins, as supported by biological and dose-dependent toxicological studies. Although individual studies have reported the biological aspects of protein-POP interactions, no comprehensive study summarizing the structural mechanisms, thermodynamics and kinetics of protein-POP complexes is available. The current review identifies and classifies protein-POP interaction according to the structural and functional basis of proteins into five major protein targets, including digestive and other enzymes, serum proteins, transcription factors, transporters, and G-protein coupled receptors. Further, analysis detailing the molecular interactions and structural mechanism evidenced that H-bonds, van der Waals, and hydrophobic interactions essentially mediate the formation of protein-POP complexes. Moreover, interaction of POPs alters the protein conformation through kinetic and thermodynamic processes like competitive inhibition and allostery to modulate the cellular signalling processes, resulting in various pathological conditions such as cancers and inflammations. In summary, the review provides a comprehensive insight into the critical structural/molecular aspects of protein-POP interactions.

PAE viewer: a webserver for the interactive visualization of the predicted aligned error for multimer structure predictions and crosslinks

The development of AlphaFold for protein structure prediction has opened a new era in structural biology. This is even more the case for AlphaFold-Multimer for the prediction of protein complexes. The interpretation of these predictions has become more important than ever, but it is difficult for the non-specialist. While an evaluation of the prediction quality is provided for monomeric protein predictions by the AlphaFold Protein Structure Database, such a tool is missing for predicted complex structures. Here, we present the PAE Viewer webserver (http://www.subtiwiki.uni-goettingen.de/v4/paeViewerDemo), an online tool for the integrated visualization of predicted protein complexes using a 3D structure display combined with an interactive representation of the Predicted Aligned Error (PAE). This metric allows an estimation of the quality of the prediction. Importantly, our webserver also allows the integration of experimental cross-linking data which helps to interpret the reliability of the structure predictions. With the PAE Viewer, the user obtains a unique online tool which for the first time allows the intuitive evaluation of the PAE for protein complex structure predictions with integrated crosslinks.

The nexus between RNA-binding proteins and their effectors

RNA-binding proteins (RBPs) regulate essentially every event in the lifetime of an RNA molecule, from its production to its destruction. Whereas much has been learned about RNA sequence specificity and general functions of individual RBPs, the ways in which numerous RBPs instruct a much smaller number of effector molecules, that is, the core engines of RNA processing, as to where, when and how to act remain largely speculative. Here, we survey the known modes of communication between RBPs and their effectors with a particular focus on converging RBP-effector interactions and their roles in reducing the complexity of RNA networks. We discern the emerging unifying principles and discuss their utility in our understanding of RBP function, regulation of biological processes and contribution to human disease.

Atomic Models of All Major Trans-Envelope Complexes Involved in Lipid Trafficking in Escherichia Coli Constructed Using a Combination of AlphaFold2, AF2Complex, and Membrane Morphing Simulations

In Gram-negative bacteria, several trans-envelope complexes (TECs) have been identified that span the periplasmic space in order to facilitate lipid transport between the inner- and outer- membranes. While partial or near-complete structures of some of these TECs have been solved by conventional experimental techniques, most remain incomplete. Here we describe how a combination of computational approaches, constrained by experimental data, can be used to build complete atomic models for four TECs implicated in lipid transport in Escherichia coli . We use DeepMind's protein structure prediction algorithm, AlphaFold2, and a variant of it designed to predict protein complexes, AF2Complex, to predict the oligomeric states of key components of TECs and their likely interfaces with other components. After obtaining initial models of the complete TECs by superimposing predicted structures of subcomplexes, we use the membrane orientation prediction algorithm OPM to predict the likely orientations of the inner- and outer- membrane components in each TEC. Since, in all cases, the predicted membrane orientations in these initial models are tilted relative to each other, we devise a novel molecular mechanics-based strategy that we call "membrane morphing" that adjusts each TEC model until the two membranes are properly aligned with each other and separated by a distance consistent with estimates of the periplasmic width in E. coli . The study highlights the potential power of combining computational methods, operating within limits set by both experimental data and by cell physiology, for producing useable atomic structures of very large protein complexes.

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches

Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protein surfaces and the development of modulators of protein-protein interactions still represent a major challenge due to their highly dynamic and extensive interfacial areas. Over the years, multiple strategies including structural, computational, and combinatorial approaches have been developed to characterize PPI and to date, several successful examples of small molecules, antibodies, peptides, and aptamers able to modulate these interfaces have been determined. Notably, peptides are a particularly useful tool for inhibiting PPIs due to their exquisite potency, specificity, and selectivity. Here, after an overview of PPIs and of the commonly used approaches to identify and characterize them, we describe and evaluate the impact of chemical peptide libraries in medicinal chemistry with a special focus on the results achieved through recent applications of this methodology. Finally, we also discuss the role that this methodology can have in the framework of the opportunities, and challenges that the application of new predictive approaches based on artificial intelligence is generating in structural biology.

Selected Approaches to Disrupting Protein-Protein Interactions within the MAPK/RAS Pathway

Within the MAPK/RAS pathway, there exists a plethora of protein-protein interactions (PPIs). For many years, scientists have focused efforts on drugging KRAS and its effectors in hopes to provide much needed therapies for patients with KRAS-mutant driven cancers. In this review, we focus on recent strategies to inhibit RAS-signaling via disrupting PPIs associated with SOS1, RAF, PDEδ, Grb2, and RAS.

Graph-BERT and language model-based framework for protein-protein interaction identification

Identification of protein-protein interactions (PPI) is among the critical problems in the domain of bioinformatics. Previous studies have utilized different AI-based models for PPI classification with advances in artificial intelligence (AI) techniques. The input to these models is the features extracted from different sources of protein information, mainly sequence-derived features. In this work, we present an AI-based PPI identification model utilizing a PPI network and protein sequences. The PPI network is represented as a graph where each node is a protein pair, and an edge is defined between two nodes if there exists a common protein between these nodes. Each node in a graph has a feature vector. In this work, we have used the language model to extract feature vectors directly from protein sequences. The feature vectors for protein in pairs are concatenated and used as a node feature vector of a PPI network graph. Finally, we have used the Graph-BERT model to encode the PPI network graph with sequence-based features and learn the hidden representation of the feature vector for each node. The next step involves feeding the learned representations of nodes to the fully connected layer, the output of which is fed into the softmax layer to classify the protein interactions. To assess the efficacy of the proposed PPI model, we have performed experiments on several PPI datasets. The experimental results demonstrate that the proposed approach surpasses the existing PPI works and designed baselines in classifying PPI.

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Population-based heteropolymer design to mimic protein mixtures

Biological fluids, the most complex blends, have compositions that constantly vary and cannot be molecularly defined1. Despite these uncertainties, proteins fluctuate, fold, function and evolve as programmed2-4. We propose that in addition to the known monomeric sequence requirements, protein sequences encode multi-pair interactions at the segmental level to navigate random encounters5,6; synthetic heteropolymers capable of emulating such interactions can replicate how proteins behave in biological fluids individually and collectively. Here, we extracted the chemical characteristics and sequential arrangement along a protein chain at the segmental level from natural protein libraries and used the information to design heteropolymer ensembles as mixtures of disordered, partially folded and folded proteins. For each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate many functions of biological fluids including assisting protein folding during translation, preserving the viability of fetal bovine serum without refrigeration, enhancing the thermal stability of proteins and behaving like synthetic cytosol under biologically relevant conditions. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity and temporal and spatial availability. This framework provides valuable guiding principles to synthetically realize protein properties, engineer bio/abiotic hybrid materials and, ultimately, realize matter-to-life transformations.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Bi-allelic MEI1 variants cause meiosis arrest and non-obstructive azoospermia

Non-obstructive azoospermia (NOA) is characterized by the failure of sperm production due to testicular disorders and represents the most severe form of male infertility. Growing evidences have indicated that gene defects could be the potential cause of NOA via genome-wide sequencing approaches. Here, bi-allelic deleterious variants in meiosis inhibitor protein 1 (MEI1) were identified by whole-exome sequencing in four Chinese patients with NOA. Testicular pathologic analysis and immunohistochemical staining revealed that spermatogenesis is arrested at spermatocyte stage, with defective programmed DNA double-strand breaks (DSBs) homoeostasis and meiotic chromosome synapsis in patients carrying the variants. In addition, our results showed that one missense variant (c.G186C) reduced the expression of MEI1 and one frameshift variant (c.251delT) led to truncated proteins of MEI1 in in vitro. Furthermore, the missense variant (c.T1585A) was assumed to affect the interaction between MEI1 and its partners via bioinformatic analysis. Collectively, our findings provide direct genetic and functional evidences that bi-allelic variants in MEI1 could cause defective DSBs homoeostasis and meiotic chromosome synapsis, which subsequently lead to meiosis arrest and male infertility. Thus, our study deepens our knowledge of the role of MEI1 in male fertility and provides a novel insight to understand the genetic aetiology of NOA.

An updated patent review of Nrf2 activators (2020-present)

INTRODUCTION

The nuclear factor erythroid 2-related factor 2 (Nrf2) is a pivotal transcription factor that controls the expression of numerous cytoprotective genes and regulates cellular defense system against oxidative insults. Thus, activating the Nrf2 pathway is a promising strategy for the treatment of various chronic diseases characterized by oxidative stress.

AREAS COVERED

This review first discusses the biological effects of Nrf2 and the regulatory mechanism of Kelch-like ECH-associated protein 1-Nrf2-antioxidant response element (Keap1-Nrf2-ARE) pathway. Then, Nrf2 activators (2020-present) are summarized based on the mechanism of action. The case studies consist of chemical structures, biological activities, structural optimization, and clinical development.

EXPERT OPINION

Extensive efforts have been devoted to developing novel Nrf2 activators with improved potency and drug-like properties. These Nrf2 activators have exhibited beneficial effects in in vitro and in vivo models of oxidative stress-related chronic diseases. However, some specific problems, such as target selectivity and brain blood barrier (BBB) permeability, still need to be addressed in the future.

MOTS-c: A promising mitochondrial-derived peptide for therapeutic exploitation

Mitochondrial ORF of the 12S rRNA Type-C (MOTS-c) is a mitochondrial-derived peptide composed of 16 amino acids encoded by the 12S rRNA region of the mitochondrial genome. The MOTS-c protein is transferred to the nucleus during metabolic stress and directs the expression of nuclear genes to promote cell balance. Different tissues co-expressed the protein with mitochondria, and plasma also contained the protein, but its level decreased with age. In addition, MOTS-c has been shown to improve glucose metabolism in skeletal muscle, which indicates its benefits for diseases such as diabetes, obesity, and aging. Nevertheless, MOTS-c has been used less frequently in disease treatment, and no effective method of applying MOTS-c in the clinic has been developed. Throughout this paper, we discussed the discovery and physiological function of mitochondrial-derived polypeptide MOTS-c, and the application of MOTS-c in the treatment of various diseases, such as aging, cardiovascular disease, insulin resistance, and inflammation. To provide additional ideas for future research and development, we tapped into the molecular mechanisms and therapeutic potentials of MOTS-c to improve diseases and combined the technology with synthetic biology in order to offer a new approach to its development and application.

Conformational switch and multiple supramolecular structures of a newly identified self-assembling protein-mimetic peptide from Pseudomonas aeruginosa YeaZ protein

Protein-mimetic peptides (PMPs) are shorter sequences of self-assembling proteins, that represent remarkable building blocks for the generation of bioinspired functional supramolecular structures with multiple applications. The identification of novel aminoacidic sequences that permit the access to valuable biocompatible materials is an attractive area of research. In this work, in silico analysis of the Pseudomonas aeruginosa YeaZ protein (PaYeaZ) led to the identification of a tetradecapeptide that represents the shortest sequence responsible for the YeaZ-YeaZ dimer formation. Based on its sequence, an innovative 20-meric peptide, called PMP-2, was designed, synthesized, and characterized in terms of secondary structure and self-assembly properties. PMP-2 conserves a helical character and self-assembles into helical nanofibers in non-polar solvents (DMSO and trifluoroethanol), as well as in dilute (0.5 mM) aqueous solutions. In contrast, at higher concentrations (>2 mM) in water, a conformational transition from α-helix to β-sheet occurs, which is accompanied by the Protein-mimetic peptide aggregation into 2D-sheets and formation supramolecular gel in aqueous environment. Our findings reveal a newly identified Protein-mimetic peptide that could turn as a promising candidate for future material applications.

Design and synthesis of a tetracyclic tripeptide mimetic frozen in a polyproline type II (PP2) helix conformation

A synthesis of the new tetracyclic scaffold ProM-19, which represents a XPP tripeptide unit frozen in a PPII helix conformation, was developed. As a key building block, N-Boc-protected ethyl (1S,3S,4R)-2-azabicyclo[2.2.1]hept-5-ene-2-carboxylate was prepared through a diastereoselective aza-Diels-Alder reaction and subsequent hydrogenolytic removal of the chiral N-1-phenylethyl substituent under temporary protection of the double bond through dihydroxylation and reconstitution by Corey-Winter olefination. The target compound Boc-[ProM-19]-OMe was then prepared via subsequent peptide coupling and Ru-catalyzed ring-closing metathesis steps employing (S)-N-Boc-allylgylcine and cis-5-vinyl-proline methyl ester as additional building blocks. In addition, Ac-[2-Cl-Phe]-[Pro]-[ProM-19]-OMe was prepared by solution phase peptide synthesis as a potential ligand for the ena-VASP EVH1 domain.

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

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

Guardians of the phloem - forisomes and beyond

The phloem is a highly specialized vascular tissue that forms a fundamentally important transport and signaling pathway in plants. It is therefore a system worth protecting. The main function of the phloem is to transport the products of photosynthesis throughout the whole plant, but it also transports soluble signaling molecules and propagates electrophysiological signals. The phloem is constantly threatened by mechanical injuries, phloem-sucking pests and parasites, and the spread of pathogens, which has led to the evolution of efficient defense mechanisms. One such mechanism involves structural phloem proteins, which are thought to facilitate sieve element occlusion following injury and to defend the plant against pathogens. In leguminous plants, specialized structural phloem proteins known as forisomes form unique mechanoproteins via sophisticated molecular interaction and assembly mechanisms, thus enabling reversible sieve element occlusion. By understanding the structure and function of forisomes and other structural phloem proteins, we can develop a toolbox for biotechnological applications in material science and medicine. Furthermore, understanding the involvement of structural phloem proteins in plant defense mechanisms will allow phloem engineering as a new strategy for the development of crop varieties that are resistant to pests, pathogens and parasites.

Interaction of client—the scaffold on which FeS clusters are build—with J-domain protein Hsc20 and its evolving Hsp70 partners

In cells molecular chaperone systems consisting of Hsp70 and its obligatory J-domain protein (JDP) co-chaperones transiently interact with a myriad of client proteins—with JDPs typically recruiting their partner Hsp70 to interact with particular clients. The fundamentals of this cyclical interactions between JDP/Hsp70 systems and clients are well established. Much less is known about other aspects of JDP/Hsp70 system function, including how such systems evolved over time. Here we discuss the JDP/Hsp70 system involved in the biogenesis of iron-sulfur (FeS) clusters. Interaction between the client protein, the scaffold on which clusters are built, and its specialized JDP Hsc20 has stayed constant. However, the system’s Hsp70 has changed at least twice. In some species Hsc20’s Hsp70 partner interacts only with the scaffold, in others it has many JDP partners in addition to Hsc20 and interacts with many client proteins. Analysis of this switching of Hsp70 partners has provided insight into the insulation of JDP/Hsp70 systems from one another that can occur when more than one Hsp70 is present in a cellular compartment, as well as how competition among JDPs is balanced when an Hsp70 partner is shared amongst a number of JDPs. Of particularly broad relevance, even though the scaffold’s interactions with Hsc20 and Hsp70 are functionally critical for the biogenesis of FeS cluster-containing proteins, it is the modulation of the Hsc20-Hsp70 interaction per se that allows Hsc20 to function with such different Hsp70 partners.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.

Protein Function Analysis through Machine Learning

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity

What are the molecular determinants of protein–protein binding affinity and whether they are similar to those regulating fold stability are two major questions of molecular biology, whose answers bring important implications both from a theoretical and applicative point of view. Here, we analyze chemical and physical features on a large dataset of protein–protein complexes with reliable experimental binding affinity data and compare them with a set of monomeric proteins for which melting temperature data was available. In particular, we probed the spatial organization of protein (1) intramolecular and intermolecular interaction energies among residues, (2) amino acidic composition, and (3) their hydropathy features. Analyzing the interaction energies, we found that strong Coulombic interactions are preferentially associated with a high protein thermal stability, while strong intermolecular van der Waals energies correlate with stronger protein–protein binding affinity. Statistical analysis of amino acids abundances, exposed to the molecular surface and/or in interaction with the molecular partner, confirmed that hydrophobic residues present on the protein surfaces are preferentially located in the binding regions, while charged residues behave oppositely. Leveraging on the important role of van der Waals interface interactions in binding affinity, we focused on the molecular surfaces in the binding regions and evaluated their shape complementarity, decomposing the molecular patches in the 2D Zernike basis. For the first time, we quantified the correlation between local shape complementarity and binding affinity via the Zernike formalism. In addition, considering the solvent interactions via the residue hydropathy, we found that the hydrophobicity of the binding regions dictates their shape complementary as much as the correlation between van der Waals energy and binding affinity. In turn, these relationships pave the way to the fast and accurate prediction and design of optimal binding regions as the 2D Zernike formalism allows a rapid and superposition-free comparison between possible binding surfaces.