Flexible Protein-Protein Docking with SwarmDock

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Antioxidant, Cytotoxicity, Antimicrobial Activity, and In Silico Analysis of the Methanolic Leaf and Flower Extracts of Clitoria ternatea

Infectious diseases pose a significant threat to human health worldwide. To address this challenge, we conducted a comprehensive study on the leaf and flower extracts of Clitoria ternatea plants. Our research encompassed in vitro assessments of their antibacterial, antibiofilm, antioxidant, and cytotoxic properties. Additionally, we employed in silico screening to identify promising compounds with potential applications in developing novel anti-Escherichia coli medications. Notably, our investigation revealed a remarkable inhibition zone of 13.00 ± 1 mm when applying the leaf extract (200 μg/ml) against E. coli, showcasing its potent antibacterial properties. Furthermore, both the leaf and flower extracts exhibited substantial biofilm inhibition efficacy against S. aureus, with inhibition percentages of 54% and 58%, respectively. In the realm of antioxidant activity, the leaf and flower extracts of C. ternatea displayed noteworthy DPPH free radical scavenging capabilities. Specifically, the leaf extract exhibited a substantial activity of 62.39% at a concentration of 150 μg/ml, while the flower extract achieved 44.08% at the same concentration. Our study also evaluated the impact on brine shrimp, where the floral extract displayed a significantly higher mortality rate of 93.33% at a dosage of 200 μg/ml compared to the leaf extract. To elucidate potential therapeutic targets, we utilized molecular docking techniques, focusing on the acbR protein (5ENR) associated with antibiotic resistance in E. coli. In this analysis, compounds isolated from the C. ternatea leaf extract, namely D1 (CID-14478556), D2 (CID-6423376), and D3 (CID-20393), exhibited binding energies of -8.2 kcal/mol, -6.5 kcal/mol, and -6.3 kcal/mol, respectively. Additionally, compounds from the flower extract, E1 (CID-5282761), E2 (CID-538757), and E3 (CID-536762), displayed binding energies of -5.4 kcal/mol, -5.3 kcal/mol, and -5.1 kcal/mol, respectively. In conclusion, the leaf and flower extracts derived from C. ternatea represent a promising natural resource with potential therapeutic applications in combating antibiotic-resistant pathogens.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling

Covalent labeling (CL) in combination with mass spectrometry can be used as an analytical tool to study and determine structural properties of protein-protein complexes. However, data from these experiments is sparse and does not unambiguously elucidate protein structure. Thus, computational algorithms are needed to deduce structure from the CL data. In this work, we present a hybrid method that combines models of protein complex subunits generated with AlphaFold with differential CL data via a CL-guided protein-protein docking in Rosetta. In a benchmark set, the RMSD (root-mean-square deviation) of the best-scoring models was below 3.6 Å for 5/5 complexes with inclusion of CL data, whereas the same quality was only achieved for 1/5 complexes without CL data. This study suggests that our integrated approach can successfully use data obtained from CL experiments to distinguish between nativelike and non-nativelike models.

Interaction Interface of Aβ42 with Human Na,K-ATPase Studied by MD and ITC and Inhibitor Screening by MD

Alzheimer’s disease (AD) is a neurodegenerative disease accompanied by progressive cognitive and memory dysfunction due to disruption of normal electrotonic properties of neurons and neuronal loss. The Na,K-ATPase interaction with beta amyloid (Aβ) plays an important role in AD pathogenesis. It has been shown that Na,K-ATPase activity in the AD brain was significantly lower than those in age-matched control brain. The interaction of Aβ₄₂ with Na,K-ATPase and subsequent oligomerization leads to inhibition of the enzyme activity. In this study interaction interfaces between three common Aβ₄₂ isoforms, and different conformations of human Na,K-ATPase (α1β1) have been obtained using molecular modeling, including docking and molecular dynamics (MD). Interaction sites of Na,K-ATPase with Aβ₄₂ are localized between extracellular parts of α- and β- subunits and are practically identical for Na,K-ATPase at different conformations. Thermodynamic parameters for the formation of Na,K-ATPase:Aβ₄₂ complex at different conformations acquired by isothermal titration calorimetry (ITC) are similar, which is in line with the data of molecular modeling. Similarity of Na,K-ATPase interaction interfaces with Aβ in all conformations allowed us to cross-screen potential inhibitors for this interaction and find pharmaceutical compounds that could block it.

Scoring of protein–protein docking models utilizing predicted interface residues

Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today's best scoring functions can significantly increase the number of top‐ranked models but still fail for most targets. Here, we examine the possibility of utilizing predicted interface residues to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the regions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. This study systematically tests different interface prediction methods for scoring >300.000 low‐resolution rigid‐body template free docking decoys. Overall we find that contact‐based interface prediction by BIPSPI is the best method to score docking solutions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high‐importance metric when estimating interface prediction quality, focusing on docking constraints production. Finally, we discussed several limitations for adopting interface predictions as constraints in a docking protocol.

Identification of a New Cholesterol-Binding Site within the IFN-γ Receptor that is Required for Signal Transduction

The cytokine interferon-gamma (IFN-γ) is a master regulator of innate and adaptive immunity involved in a broad array of human diseases that range from atherosclerosis to cancer. IFN-γ exerts it signaling action by binding to a specific cell surface receptor, the IFN-γ receptor (IFN-γR), whose activation critically depends on its partition into lipid nanodomains. However, little is known about the impact of specific lipids on IFN-γR signal transduction activity. Here, a new conserved cholesterol (chol) binding motif localized within its single transmembrane domain is identified. Through direct binding, chol drives the partition of IFN-γR2 chains into plasma membrane lipid nanodomains, orchestrating IFN-γR oligomerization and transmembrane signaling. Bioinformatics studies show that the signature sequence stands for a conserved chol-binding motif presented in many mammalian membrane proteins. The discovery of chol as the molecular switch governing IFN-γR transmembrane signaling represents a significant advance for understanding the mechanism of lipid selectivity by membrane proteins, but also for figuring out the role of lipids in modulating cell surface receptor function. Finally, this study suggests that inhibition of the chol-IFNγR2 interaction may represent a potential therapeutic strategy for various IFN-γ-dependent diseases.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

Hsp90 Inhibitors Prevent HSV-1 Replication by Directly Targeting UL42-Hsp90 Complex

Herpes simplex virus type I (HSV-1) is a member of the Alphaherpesvirinae family, which could initiate labial herpes caused by the reactivation of HSV-1 primary infection, and secondary infection even causes herpes encephalitis. The study presented here demonstrates that Hsp90 inhibitors (AT-533 and 17-AAG) directly targeted the HSV-1 UL42-Hsp90 complex, and Hsp90 interacted with HSV-1 UL42 in silicon and experiment. Interestingly, Hsp90 inhibitors also reduced virus titers of ACV-resistant clinical HSV-1 strains (153 and blue strain), revealing that HSV-1 UL42 would be a new target against ACV-resistant HSV-1 strains. Altogether, this present study indicates that Hsp90 inhibitors prevent HSV-1 proliferation by regulating the interaction between Hsp90 and HSV-1 UL42, suggesting a promising target for anti-HSV-1 therapies in the replication.

Residues R1075, D1090, R1095, and C1130 Are Critical in ADAMTS13 TSP8-Spacer Interaction Predicted by Molecular Dynamics Simulation

ADAMTS13 (A Disintegrin and Metalloprotease with Thrombospondin type 1 repeats, member 13) cleaves von Willebrand Factor (VWF) multimers to limit the prothrombotic function of VWF. The deficiency of ADAMTS13 causes a lethal thrombotic microvascular disease, thrombotic thrombocytopenic purpura (TTP). ADAMTS13 circulates in a “closed” conformation with the distal domain associating the Spacer domain to avoid off-target proteolysis or recognition by auto-antibodies. However, the interactions of the distal TSP8 domain and the Spacer domain remain elusive. Here, we constructed the TSP8-Spacer complex by a combination of homology modelling and flexible docking. Molecular dynamics simulation was applied to map the binding sites on the TSP8 or Spacer domain. The results predicted that R1075, D1090, R1095, and C1130 on the TSP8 domain were key residues that interacted with the Spacer domain. R1075 and R1095 bound exosite-4 tightly, D1090 formed multiple hydrogen bonds and salt bridges with exosite-3, and C1130 interacted with both exosite-3 and exosite-4. Specific mutations of exosite-3 (R568K/F592Y/R660K/Y661F/Y665F) or the four key residues (R1075A/D1090A/R1095A/C1130A) impaired the binding of the TSP8 domain to the Spacer domain. These results shed new light on the understanding of the auto-inhibition of ADAMTS13.

Performance and Its Limits in Rigid Body Protein-Protein Docking

The development of fast Fourier transform (FFT) algorithms enabled the sampling of billions of complex conformations and thus revolutionized protein-protein docking. FFT-based methods are now widely available and have been used in hundreds of thousands of docking calculations. Although the methods perform "soft" docking, which allows for some overlap of component proteins, the rigid body assumption clearly introduces limitations on accuracy and reliability. In addition, the method can work only with energy expressions represented by sums of correlation functions. In this paper we use a well-established protein-protein docking benchmark set to evaluate the results of these limitations by focusing on the performance of the docking server ClusPro, which implements one of the best rigid body methods. Furthermore, we explore the theoretical limits of accuracy when using established energy terms for scoring, provide comparison with flexible docking algorithms, and review the historical performance of servers in the CAPRI docking experiment.

Bayesian Active Learning for Optimization and Uncertainty Quantification in Protein Docking

Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is a lack of rigorous uncertainty quantification (UQ). To fill the gap, we introduce a novel algorithm, Bayesian active learning (BAL), for optimization and UQ of such black-box functions with applications to flexible protein docking. BAL directly models the posterior distribution of the global optimum (i.e., native structures) with active sampling and posterior estimation iteratively feeding each other. Furthermore, it uses complex normal modes to span a homogeneous, Euclidean conformation space suitable for high-dimensional optimization and constructs funnel-like energy models for quality estimation of encounter complexes. Over a protein-docking benchmark set and a CAPRI set including homology docking, we establish that BAL significantly improves against starting points from rigid docking and refinements by particle swarm optimization, providing a top-3 near-native prediction for one third targets. Quality assessment empowered with UQ leads to tight quality intervals with half range around 25% of the actual interface root-mean-square deviation and confidence level at 85%. BAL's estimated probability of a prediction being near-native achieves binary classification AUROC at 0.93 and area under the precision recall curve over 0.60 (compared to 0.50 and 0.14, respectively, by chance), which also improves ranking predictions. This study represents the first UQ solution for protein docking, with rigorous theoretical frameworks and comprehensive empirical assessments.

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

Soft regions of protein surface are potent for stable dimer formation

By having knowledge about the characteristics of protein interaction interfaces, we will be able to manipulate protein complexes for therapies. Dimer state is considered as the primary alphabet of the most proteins' quaternary structure. The properties of binding interface between subunits and of noninterface region define the specificity and stability of the intended protein complex. Considering some topological properties and amino acids' affinity for binding in interfaces of protein dimers, we construct the interface-specific recurrence plots. The data obtained from recurrence quantitative analysis, and accessibility-related metrics help us to classify the protein dimers into four distinct classes. Some mechanical properties of binding interfaces are computed for each predefined class of the dimers. The computed mechanical characteristics of binding patch region are compared with those of nonbinding region of proteins. Our observations indicate that the mechanical properties of protein binding sites have a decisive impact on determining the dimer stability. We introduce a new concept in analyzing protein structure by considering mechanical properties of protein structure. We conclude that the interface region between subunits of stable dimers is usually mechanically softer than the interface of unstable protein dimers. AbbreviationsAABaverage affinity for bindingANManisotropic network modelAPCaffinity propagation clusteringASAaccessible surface areaCCDinter residues distanceCSCcomplex stability codeDMdistance matrixΔG_dissPISA-computed dissociation free energyGNMGaussian normal mode analysisNMAnormal mode analysisPBPprotein binding patchPISAproteins, interfaces, structures and assembliesrASArelative accessible area in respect to unfolded state of residuesRMrecurrence matrixrPrelative protrusionRPrecurrence plotRQArecurrence quantitative analysisSEMstandard error of meanCommunicated by Ramaswamy H. Sarma.

What method to use for protein-protein docking?

A number of well-established servers perform 'free' docking of proteins of known structures. In contrast, template-based docking can start from sequences if structures are available for complexes that are homologous to the target. On the basis of the results of the CAPRI-CASP structure prediction experiments, template-based methods yield more accurate predictions if good templates can be found, but generally fail without such templates. However, free global docking, or focused docking around even poor quality template-based models, can still generate acceptable docked structures in these cases. In accordance with the analysis of a benchmark set, free docking of heterodimers yields acceptable or better predictions in the top 10 models for around 40% of structures. However, it is likely that a combination of template-based and free docking methods can perform better for targets that have template structures available. Another way of improving the reliability of predictions is adding experimental information as restraints, an option built into several docking servers.