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This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field. [ABSTRACT FROM AUTHOR]

Genetic code expansion enables site‐specific photo‐crosslinking by introducing photo‐reactive non‐canonical amino acids into proteins at defined positions during translation. This technology is widely used for analyzing protein–protein interactions and is applicable in mammalian cells. However, the identification of the crosslinked region still remains challenging. Here, we developed a new method to identify the crosslinked region by pre‐installing a site‐specific cleavage site, an α‐hydroxy acid (Nε‐allyloxycarbonyl‐α‐hydroxyl‐l‐lysine acid, AllocLys‐OH), into the target protein. Alkaline treatment cleaves the crosslinked complex at the position of the α‐hydroxy acid residue and thus helps to identify which side of the cleavage site, either closer to the N‐terminus or C‐terminus, the crosslinked site is located within the target protein. A series of AllocLys‐OH introductions narrows down the crosslinked region. By applying this method, we identified the crosslinked regions in lysosomal‐associated membrane protein type 2A (LAMP2A), a receptor of chaperone‐mediated autophagy, in mammalian cells. The results suggested that at least two interfaces are involved in the homophilic interaction, which requires a trimeric or higher oligomeric assembly of adjacent LAMP2A molecules. Thus, the combination of site‐specific crosslinking and site‐specific cleavage promises to be useful for revealing binding interfaces and protein complex geometries. [ABSTRACT FROM AUTHOR]

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Protein–protein interactions (PPIs) play crucial roles in many cellular processes and their deregulation often leads to cellular dysfunctions. One promising way to modulate PPIs is to use peptide derivatives that bind their protein target with high affinity and high specificity. Peptide modulators are often designed using secondary structure mimics. However, fragment-based design is an alternative emergent approach in the PPI field. Most of the reported computational fragment-based libraries targeting PPIs are composed of small molecules or already approved drugs, but, according to our knowledge, no amino acid based library has been reported yet. In this context, we developed a novel fragment-based approach called Des3PI (design of peptides targeting protein–protein interactions) with a library composed of natural amino acids. All the amino acids are docked into the target surface using Autodock Vina. The resulting binding modes are geometrically clustered, and, in each cluster, the most recurrent amino acids are identified and form the hotspots that will compose the designed peptide. This approach was applied on Ras and Mcl-1 proteins, as well as on A β protofibril. For each target, at least five peptides generated by Des3PI were tested in silico: the peptides were first blindly docked on their target, and then, the stability of the successfully docked complexes was verified using 200 ns MD simulations. Des3PI shows very encouraging results by yielding at least 3 peptides for each protein target that succeeded in passing the two-step assessment. [ABSTRACT FROM AUTHOR]

Emerged mutations tends to be closer to genetic score from wild-type. (a and b) Distribution of normalised genetic score among total and high-frequency mutations within spike protein respectively. (c) High-frequency mutations during recent surge in pandemic cases by JN.1 represented on Spike protein structure (PDB: 6VSB). (d) The distribution of normalised genetic score among ssRNA viral pandemics. [Display omitted] • Availability of large genome sequence data enables the study life-threatening viral genomes. • Triplet codons did not change very much when viruses evolve and mutate. • Computational pipeline is now available to study mutations from viral genomes. • This pipeline will provide sufficient preparedness for future pandemics that may arise. Single stranded RNA viruses are primary causative agents for pandemics, causing extensive morbidity and mortality worldwide. A pivotal question in pandemic preparedness and therapeutic intervention is what are the specific mutations which are more likely to emerge during such global health crises? This study aims to identify markers for mutations with the highest probability of emergence in these pandemics, focusing on the SARS-CoV-2 spike protein, an essential and therapeutically significant viral protein, starting from sequence information from the onset of the pandemic until July 2022. Quite consistently, we observed that emerged mutations tended to demonstrate a high genetic score, which reflects high similarity of the type of codon required for translation between an amino acid and to the mutated one. Further, this pattern is also observed in therapeutically significant proteins of other ssRNA pandemic viruses, including influenza (HA, NA), spike proteins of Ebola, envelope of Dengue and Chikungunya. We propose that the genetic score serves as an initial indicator, preceding the actual impact of the mutation on viral fitness. Finally, we developed a comprehensive computational pipeline to further explore and predict the subsequent effects of mutations on viral fitness. We believe that our pipeline can narrow down and predict future mutations in therapeutically important viral proteins during a pandemic. [ABSTRACT FROM AUTHOR]

Proteins are capable of highly specific interactions and are responsible for a wide range of functions, making them attractive in the pursuit of new therapeutic options. Previous studies focusing on overall geometry of protein–protein interfaces, however, concluded that PPI interfaces were generally flat. More recently, this idea has been challenged by their structural and thermodynamic characterisation, suggesting the existence of concave binding sites that are closer in character to traditional small-molecule binding sites, rather than exhibiting complete flatness. Here, we present a large-scale analysis of binding geometry and physicochemical properties of all protein–protein interfaces available in the Protein Data Bank. In this review, we provide a comprehensive overview of the protein–protein interface landscape, including evidence that even for overall larger, more flat interfaces that utilize discontinuous interacting regions, small and potentially druggable pockets are utilized at binding sites. [ABSTRACT FROM AUTHOR]

The chemokine receptor CXCR4 and its cognate ligand CXCL12 mediate pathways that lead to cell migration and chemotaxis. Although the structural details of related receptor-ligand complexes have been resolved, the roles of the N-terminal domain of the receptor and post-translational sulfation that are determinants of ligand selectivity and affinity remain unclear. Here, we analyze the structural dynamics induced by receptor sulfation by combining molecular dynamics, docking and network analysis. The sulfotyrosine residues, 7YsN-term, 12YsN-term and 21YsN-term allow the N-terminal domain of the apo -sulfated receptor to adopt an "open" conformation that appears to facilitate ligand binding. The overall topology of the CXCR4-CXCL12 complex is independent of the sulfation state, but an extensive network of protein-protein interactions characterizes the sulfated receptor, in line with its increased ligand affinity. The altered interactions of sulfotyrosine residues, such as 21YsN-term-47RCXCL12 replacing the 21YN-term-13FCXCL12 interaction, propagate via allosteric pathways towards the receptor lumen. In particular, our results suggest that the experimentally-reported receptor-ligand interactions 262D6.58-8RCXCL12 and 277E7.28-12RCXCL12 could be dependent on the sulfation state of the receptor and need to be carefully analyzed. Our work is an important step in understanding chemokine-receptor interactions and how post-translational modifications could modulate receptor-ligand complexes. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a fatal infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The virus infection is initiated upon recognition and binding of the spike (S) protein receptor-binding domain (RBD) to the host cell surface receptor, angiotensin-converting enzyme 2 (ACE2). Blocking the interaction between S protein and ACE2 receptor is a novel approach to prevent the viral entry into the host cell. The present study is aimed at the identification of small molecules which can disrupt the interaction between SARS-CoV-2 S protein and human ACE2 receptor by binding to the interface region. A chemical library consisting of 1,36,191 molecules were screened for drug-like compounds based on Lipinski’s rule of five, Verber’s rule and in silico toxicity parameters. The filtered drug-like molecules were next subjected to molecular docking in the interface region of RBD. The best three hits viz; ZINC64023823, ZINC33039472 and ZINC00991597 were further taken for molecular dynamics (MD) simulation studies and binding free energy evaluations using Molecular mechanics-Poisson–Boltzmann surface area (MM-PBSA) and Molecular mechanics-Generalized Born surface area (MM-GBSA). The protein-ligand complexes showed stable trajectories throughout the simulation time. ZINC33039472 exhibited binding free energy value lower as compared to the control (emodin) with a higher contribution by gas-phase energy and van der Waals energy to the total binding free energy. Thus, ZINC33039472 is identified to be a promising interfacial binding molecule which can inhibit the interaction between the viral S protein and human ACE2 receptor which would consequently help in the management of the disease.

Identification of protein-protein interfaces is necessary for understanding and regulating biological events. Genetic code expansion enables site-specific photo-cross-linking by introducing photo-reactive non-canonical amino acids into proteins at defined positions during translation. This technology is widely used for analyzing protein-protein interactions and is applicable in mammalian cells. However, the identification of the cross-linked region still remains challenging. Our new protocol enables its identification by pre-installing a site-specific cleavage site, an α-hydroxy acid (N ε -allyloxycarbonyl-α-hydroxyl-L-lysine acid, AllocLys-OH), into the target protein. Alkaline treatment cleaves the crosslinked complex at the position of the α-hydroxy acid residue and thus helps to identify which side of the cleavage site, either closer to the N-terminus or C-terminus, the crosslinked site is located on within the target protein. A series of AllocLys-OH introductions narrows down the crosslinked region. This combination of site-specific crosslinking and cleavage promises to be useful for revealing binding interfaces and protein complex geometries. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Search for crosslinkable sites Basic Protocol 2: Site-specific photo-cross-linking/cleavage., (© 2024 Wiley Periodicals LLC.)

Leucine rich repeat kinase 2 (LRRK2) is the most common genetic contributor to Parkinson's disease (PD), a complex neurodegenerative disorder affecting nearly 10 million people worldwide. Pathogenic mutations within LRRK2 often induce increased kinase activity, an effect that can be abolished with many small molecule inhibitors; however, these small molecule inhibitors are currently limited by their toxicities. Given the large and complex nature of LRRK2, more recent efforts have focused on protein–protein interactions (PPIs) involving LRRK2 and how they can contribute to PD. Here, we review recently resolved structures of LRRK2 and highlight unique interfaces driving both catalytic and non‐catalytic activities. Combining new structural information with established in vitro and in vivo data clarifies the role of PPIs in driving LRRK2‐mediated disease pathogenesis. Since constrained peptides and peptidomimetics have the potential to engage with elongated, hydrophobic interfaces that were previously considered "undruggable," they may provide a unique handle for LRRK2 targeting. Here, we discuss the use of constrained peptides and peptidomimetics to target LRRK2 as a strategy to downregulate its pathological activity. [ABSTRACT FROM AUTHOR]

ABSTRACT Human health is threatened by bacterial infections that are increasingly resistant to multiple drugs. A recently emerged strategy consists of disarming pathogenic bacteria by targeting and blocking their virulence factors. The type VI secretion system (T6SS) is a widespread secretion nanomachine encoded and employed by pathogenic strains to establish their virulence process during host invasion. Given the conservation of T6SS in several human bacterial pathogens, the discovery of an effective broad-spectrum T6SS virulence blocker represents an attractive target for development of antivirulence therapies. Here, we identified and validated a protein-protein interaction interface, TssK-TssG, as a key factor in the assembly of the T6SS baseplate (BP) complex in the pathogen enteroaggregative Escherichia coli (EAEC). In silico and biochemical studies revealed that the determinants of the interface are broadly conserved among pathogenic species, suggesting a role for this interface as a target for T6SS inhibition. Based on the high-resolution structure of the TssKFGE wedge complex, we rationally designed a biomimetic cyclic peptide (BCP) that blocks the assembly of the EAEC BP complex and inhibits the function of T6SS in bacterial cultures. Our BCP is the first compound completely designed from prior structural knowledge with anti-T6SS activity that can be used as a model to target human pathogens. IMPORTANCE New therapeutic options are urgently needed to fight drug-resistant and life-threatening infections. In contrast to antibiotics that inhibit the growth pathways of bacteria, the antivirulence strategy is a promising approach to disarm pathogens by interfering with bacterial virulence factors without exerting evolutionary pressure. The type VI secretion system (T6SS) is used by many pathogens, including members of the antibiotic-resistant ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.), to establish their virulence during the invasion of the human host. Although the T6SS is undoubtedly involved in pathogenesis, strategies targeting this virulence factor are crucially lacking. Here, we used a combination of genetics, microbiology, biochemical, biophysics, and bioinformatics approaches to rationally design a biomimetic peptide that interferes with T6SS assembly and functioning. This study represents a novel proof of concept for an antivirulence strategy which aims to interfere with the assembly of the T6SS.

Membrane proteins play a variety of biological functions to the survival of organisms and functionalities of these proteins are often due to their homo‐ or hetero‐complexation. Encoded by ~30% of the genome in most organisms, they represent the target of over half of nowadays drugs. Spanning the entirety of the cell membrane, transmembrane proteins are the most common type of membrane proteins and can be classified by secondary structures: alpha‐helical and beta‐barrel structures. Protein‐protein interaction (PPI) have been widely studied for globular proteins and many computational tools are available for predicting PPI sites and construct models of complexes. Here, the structural regions of a non‐redundant set of 232 alpha‐helical and 37 beta‐barrel transmembrane complexes and their interfaces are analyzed. Using the residue composition, frequency and propensity, this study brings the light on the marker residue types located at the structural regions of alpha‐helical and beta‐barrel transmembrane homomeric protein complexes and of their interfaces. This study also shows the necessity to relate the frequency to the composition into a ratio for immediately figuring out residue types presenting high frequencies at the interface and/or at one of its structural regions despite being a minor contributor compared to other residue types to that location's residue composition. [ABSTRACT FROM AUTHOR]

Abstract Background Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. Results In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average I rmsd value of 3.28Å and overall F nat value of 63%, which improves upon I rmsd of 3.89Å and F nat of 49% for ZRANK, and I rmsd of 3.99Å and F nat of 46% for ClusPro. On CAPRI targets, our method achieves average I rmsd value of 3.45Å and overall F nat value of 46%, which improves upon I rmsd of 4.18Å and F nat of 40% for ZRANK, and I rmsd of 5.12Å and F nat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. Conclusion Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.

Tyrosinase (TYR) is a copper-containing monooxygenase central to the function of melanocytes. Alterations in its expression or activity contribute to variations in skin, hair and eye color, and underlie a variety of pathogenic pigmentary phenotypes, including several forms of oculocutaneous albinism (OCA). Many of these phenotypes are linked to individual missense mutations causing single nucleotide variants and polymorphisms (SNVs) in TYR. We previously showed that two TYR homologues, TYRP1 and TYRP2, modulate TYR activity and stabilize the TYR protein. Accordingly, to investigate whether TYR, TYRP1, and TYRP2 are biophysically compatible with various heterocomplexes, we computationally docked a high-quality 3D model of TYR to the crystal structure of TYRP1 and to a high-quality 3D model of TYRP2. Remarkably, the resulting TYR-TYRP1 heterodimer was complementary in structure and energy with the TYR-TYRP2 heterodimer, with TYRP1 and TYRP2 docking to different adjacent surfaces on TYR that apposed a third realistic protein interface between TYRP1-TYRP2. Hence, the 3D models are compatible with a heterotrimeric TYR-TYRP1-TYRP2 complex. In addition, this heterotrimeric TYR-TYRP1-TYRP2 positioned the C-terminus of each folded enzymatic domain in an ideal position to allow their C-terminal transmembrane helices to form a putative membrane embedded three-helix bundle. Finally, pathogenic TYR mutations causing OCA1A, which also destabilize TYR biochemically, cluster on an unoccupied protein interface at the periphery of the heterotrimeric complex, suggesting that this may be a docking site for OCA2, an anion channel. Pathogenic OCA2 mutations result in similar phenotypes to those produced by OCA1A TYR mutations. While this complex may be difficult to detect in vitro, due to the complex environment of the vertebrate cellular membranous system, our results support the existence of a heterotrimeric complex in melanogenesis.

• The research highlights a novel strategy to block SARS-CoV-2 entry into the host cell. • High-throughput virtual screening identifies ZINC33039472 as a small molecule with favourable drug-like properties. • It shows higher binding affinity to the spike RBD interface region compared to emodin. • ZINC33039472 could be a potential drug candidate molecule for COVID-19. Coronavirus disease 2019 (COVID-19) is a fatal infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The virus infection is initiated upon recognition and binding of the spike (S) protein receptor-binding domain (RBD) to the host cell surface receptor, angiotensin-converting enzyme 2 (ACE2). Blocking the interaction between S protein and ACE2 receptor is a novel approach to prevent the viral entry into the host cell. The present study is aimed at the identification of small molecules which can disrupt the interaction between SARS-CoV-2 S protein and human ACE2 receptor by binding to the interface region. A chemical library consisting of 1,36,191 molecules were screened for drug-like compounds based on Lipinski's rule of five, Verber's rule and in silico toxicity parameters. The filtered drug-like molecules were next subjected to molecular docking in the interface region of RBD. The best three hits viz; ZINC64023823, ZINC33039472 and ZINC00991597 were further taken for molecular dynamics (MD) simulation studies and binding free energy evaluations using Molecular mechanics-Poisson–Boltzmann surface area (MM-PBSA) and Molecular mechanics-Generalized Born surface area (MM-GBSA). The protein-ligand complexes showed stable trajectories throughout the simulation time. ZINC33039472 exhibited binding free energy value lower as compared to the control (emodin) with a higher contribution by gas-phase energy and van der Waals energy to the total binding free energy. Thus, ZINC33039472 is identified to be a promising interfacial binding molecule which can inhibit the interaction between the viral S protein and human ACE2 receptor which would consequently help in the management of the disease. [ABSTRACT FROM AUTHOR]

Abstract Background Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems. Results In this work, we are providing new insights into the differences between biological and crystallographic complexes by focusing on “pair-properties” of interfaces that have not yet been fully investigated. We investigated properties such intermolecular residue-residue contacts (already successfully applied to the prediction of binding affinities) and interaction energies (electrostatic, Van der Waals and desolvation). By using the XtalMany and BioMany interface datasets, we show that interfacial residue contacts, classified as a function of their physico-chemical properties, can distinguish between biological and crystallographic interfaces. The energetic terms show, on average, higher values for crystal interfaces, reflecting a less stable interface due to crystal packing compared to biological interfaces. By using a variety of machine learning approaches, we trained a new interface classification predictor based on contacts and interaction energetic features. Our predictor reaches an accuracy in classifying biological vs crystal interfaces of 0.92, compared to 0.88 for EPPIC (one of the main state-of-the-art classifiers reporting same performance as PISA). Conclusion In this work we have gained insights into the nature of intermolecular contacts and energetics terms distinguishing biological from crystallographic interfaces. Our findings might have a broader applicability in structural biology, for example for the identification of near native poses in docking. We implemented our classification approach into an easy-to-use and fast software, freely available to the scientific community from http://github.com/haddocking/interface-classifier.

Abstract Background Hot spots are interface residues that contribute most binding affinity to protein-protein interaction. A compact and relevant feature subset is important for building machine learning methods to predict hot spots on protein-protein interfaces. Although different methods have been used to detect the relevant feature subset from a variety of features related to interface residues, it is still a challenge to detect the optimal feature subset for building the final model. Results In this study, three different feature selection methods were compared to propose a new hybrid feature selection strategy. This new strategy was proved to effectively reduce the feature space when we were building the prediction models for identifying hotspot residues. It was tested on eighty-two features, both conventional and newly proposed. According to the strategy, combining the feature subsets selected by decision tree and mRMR (maximum Relevance Minimum Redundancy) individually, we were able to build a model with 6 features by using a PSFS (Pseudo Sequential Forward Selection) process. Compared with other state-of-art methods for the independent test set, our model had shown better or comparable predictive performances (with F-measure 0.622 and recall 0.821). Analysis of the 6 features confirmed that our newly proposed feature CNSV_REL1 was important for our model. The analysis also showed that the complementarity between features should be considered as an important aspect when conducting the feature selection. Conclusion In this study, most important of all, a new strategy for feature selection was proposed and proved to be effective in selecting the optimal feature subset for building prediction models, which can be used to predict hot spot residues on protein-protein interfaces. Moreover, two aspects, the generalization of the single feature and the complementarity between features, were proved to be of great importance and should be considered in feature selection methods. Finally, our newly proposed feature CNSV_REL1 had been proved an alternative and effective feature in predicting hot spots by our study. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPPHOT/ .

Background: Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. Results: In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average Irmsd value of 3.28Å and overall Fnat value of 63%, which improves upon Irmsd of 3.89Å and Fnat of 49% for ZRANK, and Irmsd of 3.99Å and Fnat of 46% for ClusPro. On CAPRI targets, our method achieves average Irmsd value of 3.45Å and overall Fnat value of 46%, which improves upon Irmsd of 4.18Å and Fnat of 40% for ZRANK, and Irmsd of 5.12Å and Fnat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. Conclusion: Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface. [ABSTRACT FROM AUTHOR]

It is an important goal of computational biology to correctly predict the association state of a protein based on its amino acid sequence and the structures of known homologues. We have pursued this goal on the example of anthranilate phosphoribosyltransferase (AnPRT), an enzyme that is involved in the biosynthesis of the amino acid tryptophan. Firstly, known crystal structures of naturally occurring homodimeric AnPRTs were analyzed using the Protein Interfaces, Surfaces, and Assemblies (PISA) service of the European Bioinformatics Institute (EBI). This led to the identification of two hydrophobic "hot spot" amino acids in the protein‐protein interface that were predicted to be essential for self‐association. Next, in a comprehensive multiple sequence alignment (MSA), naturally occurring AnPRT variants with hydrophilic or charged amino acids in place of hydrophobic residues in the two hot spot positions were identified. Representative variants were characterized in terms of thermal stability, enzymatic activity, and quaternary structure. We found that AnPRT variants with charged residues in both hot spot positions exist exclusively as monomers in solution. Variants with hydrophilic amino acids in one hot spot position occur in both forms, monomer and dimer. The results of the present study provide a detailed characterization of the determinants of the AnPRT monomer‐dimer equilibrium and show that analysis of hot spots in combination with MSAs can be a valuable tool in prediction of protein quaternary structures. [ABSTRACT FROM AUTHOR]

PDGF-BB/PDGFR-ββ signaling plays very crucial roles in the process of many diseases such as liver fibrosis. However, drug candidates with selective affinities for PDGF-B/PDGFR-β remain deficient. Here, we identified a natural cyclopeptide termed destruxin A5 that effectively inhibits PDGF-BB-induced PDGFR-β signaling. Interestingly and importantly, the inhibitory mechanism is distinct from the mechanism of tyrosine kinase inhibitors because destruxin A5 does not have the ability to bind to the ATP-binding pocket of PDGFR-β. Using Biacore T200 technology, thermal shift technology, microscale thermophoresis technology and computational analysis, we confirmed that destruxin A5 selectively targets the PDGF-B/PDGFR-β interaction interface to block this signaling. Additionally, the inhibitory effect of destruxin A5 on PDGF-BB/PDGFR-ββ signaling was verified using in vitro, ex vivo and in vivo models, in which the extent of liver fibrosis was effectively alleviated by destruxin A5. In summary, destruxin A5 may represent an efficacious and more selective inhibitor of PDGF-BB/PDGFR-ββ signaling.

Complexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structural characterization of protein-protein complexes. Correctly identifying the biologically relevant interface from the crystallographic ones is, however, not trivial and can be prone to errors. Over the past two decades, computational methodologies have been developed to study the differences of those interfaces and automatically classify them as biological or crystallographic. Overall, protein-protein interfaces show differences in terms of composition, energetics and evolutionary conservation between biological and crystallographic ones. Based on those observations, a number of computational methods have been developed for this classification problem, which can be grouped into three main categories: Energy-, empirical knowledge- and machine learning-based approaches. In this review, we give a comprehensive overview of the training datasets and methods so far implemented, providing useful links and a brief description of each method.

Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Genetic code expansion enables site-specific photo-crosslinking by introducing photo-reactive non-canonical amino acids into proteins at defined positions during translation. This technology is widely used for analyzing protein-protein interactions and is applicable in mammalian cells. However, the identification of the crosslinked region still remains challenging. Here, we developed a new method to identify the crosslinked region by pre-installing a site-specific cleavage site, an α-hydroxy acid (N ε -allyloxycarbonyl-α-hydroxyl-l-lysine acid, AllocLys-OH), into the target protein. Alkaline treatment cleaves the crosslinked complex at the position of the α-hydroxy acid residue and thus helps to identify which side of the cleavage site, either closer to the N-terminus or C-terminus, the crosslinked site is located within the target protein. A series of AllocLys-OH introductions narrows down the crosslinked region. By applying this method, we identified the crosslinked regions in lysosomal-associated membrane protein type 2A (LAMP2A), a receptor of chaperone-mediated autophagy, in mammalian cells. The results suggested that at least two interfaces are involved in the homophilic interaction, which requires a trimeric or higher oligomeric assembly of adjacent LAMP2A molecules. Thus, the combination of site-specific crosslinking and site-specific cleavage promises to be useful for revealing binding interfaces and protein complex geometries., (© 2023 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

Background: Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems. Results: In this work, we are providing new insights into the differences between biological and crystallographic complexes by focusing on "pair-properties" of interfaces that have not yet been fully investigated. We investigated properties such intermolecular residue-residue contacts (already successfully applied to the prediction of binding affinities) and interaction energies (electrostatic, Van der Waals and desolvation). By using the XtalMany and BioMany interface datasets, we show that interfacial residue contacts, classified as a function of their physico-chemical properties, can distinguish between biological and crystallographic interfaces. The energetic terms show, on average, higher values for crystal interfaces, reflecting a less stable interface due to crystal packing compared to biological interfaces. By using a variety of machine learning approaches, we trained a new interface classification predictor based on contacts and interaction energetic features. Our predictor reaches an accuracy in classifying biological vs crystal interfaces of 0.92, compared to 0.88 for EPPIC (one of the main state-of-the-art classifiers reporting same performance as PISA). Conclusion: In this work we have gained insights into the nature of intermolecular contacts and energetics terms distinguishing biological from crystallographic interfaces. Our findings might have a broader applicability in structural biology, for example for the identification of near native poses in docking. We implemented our classification approach into an easy-to-use and fast software, freely available to the scientific community from http://github.com/haddocking/interface-classifier. [ABSTRACT FROM AUTHOR]

Crystallographic structures of protein complexes are essential to develop proteomic and structural biology methods, as prediction of protein–protein interaction (PPI) sites and protein–protein docking. Such structures can aid the development of protein complexation inhibitors. Complex DataBase (CDB), accessible at www.jct-bioinfo.com/cdb/search, is a database web application for heterodimeric protein crystallographic complexes along with the crystallographic structures of each individual unbound protein. Direct access to crystallographic structures of protein complexes, along with provided annotations, can serve as starting point for constructing new experimental protein complexes sets of any type, for protein binding studies, and the development and evaluation of PPIs prediction methods. [ABSTRACT FROM AUTHOR]

Understanding the relationship between protein sequence, function, and stability is a fundamental problem in biology. The essential function of many proteins that fold into a specific structure is their ability to bind to a ligand, which can be assayed for thousands of mutated variants. However, binding assays do not distinguish whether mutations affect the stability of the binding interface or the overall fold. Here, we introduce a statistical method to infer a detailed energy landscape of how a protein folds and binds to a ligand by combining information from many mutated variants. We fit a thermodynamic model describing the bound, unbound, and unfolded states to high quality data of protein G domain B1 binding to IgG-Fc. We infer distinct folding and binding energies for each mutation providing a detailed view of how mutations affect binding and stability across the protein. We accurately infer the folding energy of each variant in physical units, validated by independent data, whereas previous high-throughput methods could only measure indirect changes in stability. While we assume an additive sequence–energy relationship, the binding fraction is epistatic due its nonlinear relation to energy. Despite having no epistasis in energy, our model explains much of the observed epistasis in binding fraction, with the remaining epistasis identifying conformationally dynamic regions. [ABSTRACT FROM AUTHOR]

Abstract: We perform molecular dynamics simulation studies on interaction between bacterial proteins: an outer‐membrane protein STY3179 and a yfdX protein STY3178 of Salmonella Typhi. STY3179 has been found to be involved in bacterial adhesion and invasion. STY3178 is recently biophysically characterized. It is a soluble protein having antibiotic binding and chaperon activity capabilities. These two proteins co‐occur and are from neighboring gene in Salmonella Typhi‐occurrence of homologs of both STY3178 and STY3179 are identified in many Gram‐negative bacteria. We show using homology modeling, docking followed by molecular dynamics simulation that they can form a stable complex. STY3178 belongs to aqueous phase, while the beta barrel portion of STY3179 remains buried in DPPC bilayer with extra‐cellular loops exposed to water. To understand the molecular basis of interaction between STY3178 and STY3179, we compute the conformational thermodynamics which indicate that these two proteins interact through polar and acidic residues belonging to their interfacial region. Conformational thermodynamics results further reveal instability of certain residues in extra‐cellular loops of STY3179 upon complexation with STY3178 which is an indication for binding with host cell protein laminin. [ABSTRACT FROM AUTHOR]

[Display omitted] • The first crystal structure of a yeast septin heterodimer (Cdc3-Cdc10) provides important insights into their structural biology. • Identification of common features and differences between yeast and human septins, sheds light on the unique characteristics of yeast septin filaments. • The Cdc3G-Cdc10 Δ1-10 crystal structure could be a crucial piece of the puzzle towards obtaining a high-resolution cryo-EM structure of the yeast septin octamer. Septins, often described as the fourth component of the cytoskeleton, are structural proteins found in a vast variety of living beings. They are related to small GTPases and thus, generally, present GTPase activity which may play an important (although incompletely understood) role in their organization and function. Septins polymerize into long non-polar filaments, in which each subunit interacts with two others by alternating interfaces, NC and G. In Saccharomyces cerevisiae four septins are organized in the following manner, [Cdc11-Cdc12-Cdc3-Cdc10-Cdc10-Cdc3-Cdc12-Cdc11] n in order to form filaments. Although septins were originally discovered in yeast and much is known regarding their biochemistry and function, only limited structural information about them is currently available. Here we present crystal structures of Cdc3/Cdc10 which provide the first view of the physiological interfaces formed by yeast septins. The G-interface has properties which place it in between that formed by SEPT2/SEPT6 and SEPT7/SEPT3 in human filaments. Switch I from Cdc10 contributes significantly to the interface, whereas in Cdc3 it is largely disorded. However, the significant negative charge density of the latter suggests it may have a unique role. At the NC-interface, we describe an elegant means by which the sidechain of a glutamine from helix α 0 imitates a peptide group in order to retain hydrogen-bond continuity at the kink between helices α 5 and α 6 in the neighbouring subunit, thereby justifying the conservation of the helical distortion. Its absence from Cdc11, along with this structure's other unusual features are critically discussed by comparison with Cdc3 and Cdc10. [ABSTRACT FROM AUTHOR]

Background: Hot spots are interface residues that contribute most binding affinity to protein-protein interaction. A compact and relevant feature subset is important for building machine learning methods to predict hot spots on protein-protein interfaces. Although different methods have been used to detect the relevant feature subset from a variety of features related to interface residues, it is still a challenge to detect the optimal feature subset for building the final model. Results: In this study, three different feature selection methods were compared to propose a new hybrid feature selection strategy. This new strategy was proved to effectively reduce the feature space when we were building the prediction models for identifying hotspot residues. It was tested on eighty-two features, both conventional and newly proposed. According to the strategy, combining the feature subsets selected by decision tree and mRMR (maximum Relevance Minimum Redundancy) individually, wewere able to build a model with 6 features by using a PSFS (Pseudo Sequential Forward Selection) process. Compared with other state-of-art methods for the independent test set, our model had shown better or comparable predictive performances (with F-measure 0.622 and recall 0.821). Analysis of the 6 features confirmed that our newly proposed feature CNSV_REL1 was important for our model. The analysis also showed that the complementarity between features should be considered as an important aspect when conducting the feature selection. Conclusion: In this study, most important of all, a new strategy for feature selection was proposed and proved to be effective in selecting the optimal feature subset for building prediction models, which can be used to predict hot spot residues on protein-protein interfaces. Moreover, two aspects, the generalization of the single feature and the complementarity between features, were proved to be of great importance and should be considered in feature selection methods. Finally, our newly proposed feature CNSV_REL1 had been proved an alternative and effective feature in predicting hot spots by our study. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPPHOT/. [ABSTRACT FROM AUTHOR]

Interactions between protein molecules are essential for the assembly, function, and regulation of proteins. The contact region between two protein molecules in a protein complex is usually complementary in shape for both molecules and the area of the contact region can be used to estimate the binding strength between two molecules. Although the area is a value calculated from the three-dimensional surface, it cannot represent the three-dimensional shape of the surface. Therefore, we propose an original concept of two-dimensional contact area which provides further information such as the ruggedness of the contact region. We present a novel algorithm for calculating the binding direction between two molecules in a protein complex, and then suggest a method to compute the two-dimensional flattened area of the contact region between two molecules based on the binding direction. [ABSTRACT FROM AUTHOR]

ABSTRACT The recently developed statistical measure for the type of residue-residue contact at protein complex interfaces, based on a parameter-free definition of contact, has been used to define a contact score that is correlated with the likelihood of correctness of a proposed complex structure. Comparing the proposed contact scores on the native structure and on a set of model structures the proposed measure was shown to generally favor the native structure but in itself was not able to reliably score the native structure to be the best. Adjusting the scores of redocking experiments with the contact score showed that the adjusted score was able to move up the ranking of the native-like structure among the proposed complexes when the native-like was not ranked the best by the respective program. Tests on docking of unbound proteins compared the contact scores of the complexes with the contact score of the crystal structure again showing the tendency of the contact score to favor native-like conformations. The possibility of using the contact score to improve the determination of biological dimers in a crystal structure was also explored. Proteins 2017; 85:235-241. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Protein-protein interface residues, especially those at the core of the interface, exhibit higher conservation than residues in solvent exposed regions. Here, we explore the ability of this differential conservation to evaluate fittings of atomic models in low-resolution cryo-EM maps and select models from the ensemble of solutions that are often proposed by different model fitting techniques. As a prelude, using a non-redundant and high-resolution structural dataset involving 125 permanent and 95 transient complexes, we confirm that core interface residues are conserved significantly better than nearby non-interface residues and this result is used in the cryo-EM map analysis. From the analysis of inter-component interfaces in a set of fitted models associated with low-resolution cryo-EM maps of ribosomes, chaperones and proteasomes we note that a few poorly conserved residues occur at interfaces. Interestingly a few conserved residues are not in the interface, though they are close to the interface. These observations raise the potential requirement of refitting the models in the cryo-EM maps. We show that sampling an ensemble of models and selection of models with high residue conservation at the interface and in good agreement with the density helps in improving the accuracy of the fit. This study indicates that evolutionary information can serve as an additional input to improve and validate fitting of atomic models in cryo-EM density maps. [ABSTRACT FROM AUTHOR]

The Vaccinia virus polymerase holoenzyme is composed of three subunits: E9, the catalytic DNA polymerase subunit; D4, a uracil-DNA glycosylase; and A20, a protein with no known enzymatic activity. The D4/A20 heterodimer is the DNA polymerase cofactor, the function of which is essential for processive DNA synthesis. The recent crystal structure of D4 bound to the first 50 amino acids of A20 (D4/A201-50) revealed the importance of three residues, forming a cation-π interaction at the dimerization interface, for complex formation. These are Arg167 and Pro173 of D4 and Trp43 of A20. Here, the crystal structures of the three mutants D4-R167A/A201-50, D4-P173G/A201-50 and D4/A201-50-W43A are presented. The D4/A20 interface of the three structures has been analysed for atomic solvation parameters and cation-π interactions. This study confirms previous biochemical data and also points out the importance for stability of the restrained conformational space of Pro173. Moreover, these new structures will be useful for the design and rational improvement of known molecules targeting the D4/A20 interface. [ABSTRACT FROM AUTHOR]

Studies on protein–protein interaction are important in proteome research. How to build more effective models based on sequence information, structure information and physicochemical characteristics, is the key technology in protein–protein interface prediction. In this paper, we study the protein–protein interface prediction problem. We propose a novel method for identifying residues on interfaces from an input protein with both sequence and 3D structure information, based on hexagon structure similarity. Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein–protein interface. Comparing to existing methods, our approach improves F -measure value by at least 0.03. On a common dataset consisting of 41 complexes, our method has overall precision and recall values of 63% and 57%. On Benchmark v4.0, our method has overall precision and recall values of 55% and 56%. On CAPRI targets, our method has overall precision and recall values of 52% and 55%. [ABSTRACT FROM AUTHOR]

Recent advances in DNA sequencing techniques have identified rare single-nucleotide variants with less than 1% minor allele frequency. Despite the growing interest and physiological importance of rare variants in genome sciences, less attention has been paid to the allele frequency of variants in protein sciences. To elucidate the characteristics of genetic variants on protein interaction sites, from the viewpoints of the allele frequency and the structural position of variants, we mapped about 20,000 human SNVs onto protein complexes. We found that variants are less abundant in protein interfaces, and specifically the core regions of interfaces. The tendency to 'avoid' the interfacial core is stronger among common variants than rare variants. As amino acid substitutions, the trend of mutating amino acids among rare variants is consistent in different interfacial regions, reflecting the fact that rare variants result from random mutations in DNA sequences, whereas amino acid changes of common variants vary between the interfacial core and rim regions, possibly due to functional constraints on proteins. This study illustrated how the allele frequency of variants relates to the protein structural regions and the functional sites in general and will lead to deeper understanding of the potential deleteriousness of rare variants at the structural level. Exceptional cases of the observed trends will shed light on the limitations of structural approaches to evaluate the functional impacts of variants. [ABSTRACT FROM AUTHOR]

We present an updated and integrated version of our widely used protein–protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein–protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody–antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r = 0.52 overall and r = 0.72 for the rigid complexes. [ABSTRACT FROM AUTHOR]

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented. [ABSTRACT FROM AUTHOR]

Bacterial pathogens such as Pseudomonas aeruginosa use complex regulatory networks to tailor gene expression patterns to meet complex environmental challenges. P. aeruginosa is capable of causing both acute and chronic persistent infections, each type being characterized by distinct symptoms brought about by distinct sets of virulence mechanisms. The GacS/GacA phosphorelay system sits at the heart of a complex regulatory network that reciprocally governs the expression of virulence factors associated with either acute or chronic infections. A second non-enzymatic signaling cascade involving four proteins, ExsA, ExsC, ExsD, and ExsE is a key player in regulating the expression of the type three secretion system, an essential facilitator of acute infections. Both signaling pathways involve a remarkable array of non-canonical interactions that we sought to characterize. In the following section, we will outline several strategies, we adapted to map protein-protein interfaces and quantify the strength of biomolecular interactions by pairing complex mutational analyses with FRET binding assays and Bacterial-Two-Hybrid assays with appropriate functional assays. In the process, protocols were developed for disrupting large hydrophobic interfaces, deleting entire domains within a protein, and for mapping protein-protein interfaces formed primarily through backbone interactions., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Identification of drug-like small molecules that alter protein–protein interactions might be a key step in drug discovery. However, it is very challenging to find such molecules that target interface regions in protein complexes. Recent findings indicate that such molecules usually target specifically energetically favored residues (hot spots) in protein–protein interfaces. These residues contribute to the stability of protein–protein complexes. Computational prediction of hot spots on bound and unbound structures might be useful to find druggable sites on target interfaces. We review the recent advances in computational hot spot prediction methods in the first part of the review and then provide examples on how hot spots might be crucial in drug design. [ABSTRACT FROM AUTHOR]