The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

In silico prediction of host-pathogen protein interactions in melioidosis pathogen Burkholderia pseudomallei and human reveals novel virulence factors and their targets.

The aerobic, Gram-negative motile bacillus, Burkholderia pseudomallei is a facultative intracellular bacterium causing melioidosis, a critical disease of public health importance, which is widely endemic in the tropics and subtropical regions of the world. Melioidosis is associated with high case fatality rates in animals and humans; even with treatment, its mortality is 20-50%. It also infects plants and is designated as a biothreat agent. B. pseudomallei is pathogenic due to its ability to invade, resist factors in serum and survive intracellularly. Despite its importance, to date only a few effector proteins have been functionally characterized, and there is not much information regarding the host-pathogen protein-protein interactions (PPI) of this system, which are important to studying infection mechanisms and thereby develop prevention measures. We explored two computational approaches, the homology-based interolog and the domain-based method, to predict genome-scale host-pathogen interactions (HPIs) between two different strains of B. pseudomallei (prototypical, and highly virulent) and human. In total, 76 335 common HPIs (between the two strains) were predicted involving 8264 human and 1753 B. pseudomallei proteins. Among the unique PPIs, 14 131 non-redundant HPIs were found to be unique between the prototypical strain and human, compared to 3043 non-redundant HPIs between the highly virulent strain and human. The protein hubs analysis showed that most B. pseudomallei proteins formed a hub with human dnaK complex proteins associated with tuberculosis, a disease similar in symptoms to melioidosis. In addition, drug-binding and carbohydrate-binding mechanisms were found overrepresented within the host-pathogen network, and metabolic pathways were frequently activated according to the pathway enrichment. Subcellular localization analysis showed that most of the pathogen proteins are targeting human proteins inside cytoplasm and nucleus. We also discovered the host targets of the drug-related pathogen proteins and proteins that form T3SS and T6SS in B. pseudomallei. Additionally, a comparison between the unique PPI patterns present in the prototypical and highly virulent strains was performed. The current study is the first report on developing a genome-scale host-pathogen protein interaction networks between the human and B. pseudomallei, a critical biothreat agent. We have identified novel virulence factors and their interacting partners in the human proteome. These PPIs can be further validated by high-throughput experiments and may give new insights on how B. pseudomallei interacts with its host, which will help medical researchers in developing better prevention measures.

Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis.

BACKGROUND: A comprehensive map of the human-M. tuberculosis (MTB) protein interactome would help fill the gaps in our understanding of the disease, and computational prediction can aid and complement experimental studies towards this end. Several sequence-based in silico approaches tap the existing data on experimentally validated protein-protein interactions (PPIs); these PPIs serve as templates from which novel interactions between pathogen and host are inferred. Such comparative approaches typically make use of local sequence alignment, which, in the absence of structural details about the interfaces mediating the template interactions, could lead to incorrect inferences, particularly when multi-domain proteins are involved. RESULTS: We propose leveraging the domain-domain interaction (DDI) information in PDB complexes to score and prioritize candidate PPIs between host and pathogen proteomes based on targeted sequence-level comparisons. Our method picks out a small set of human-MTB protein pairs as candidates for physical interactions, and the use of functional meta-data suggests that some of them could contribute to the in vivo molecular cross-talk between pathogen and host that regulates the course of the infection. Further, we present numerical data for Pfam domain families that highlights interaction specificity on the domain level. Not every instance of a pair of domains, for which interaction evidence has been found in a few instances (i.e. structures), is likely to functionally interact. Our sorting approach scores candidates according to how "distant" they are in sequence space from known examples of DDIs (templates). Thus, it provides a natural way to deal with the heterogeneity in domain-level interactions. CONCLUSIONS: Our method represents a more informed application of local alignment to the sequence-based search for potential human-microbial interactions that uses available PPI data as a prior. Our approach is somewhat limited in its sensitivity by the restricted size and diversity of the template dataset, but, given the rapid accumulation of solved protein complex structures, its scope and utility are expected to keep steadily improving.

Interdomain hydrophobic interactions modulate the thermostability of microbial esterases from the hormone-sensitive lipase family.

Microbial hormone-sensitive lipases (HSLs) contain a CAP domain and a catalytic domain. However, it remains unclear how the CAP domain interacts with the catalytic domain to maintain the stability of microbial HSLs. Here, we isolated an HSL esterase, E40, from a marine sedimental metagenomic library. E40 exhibited the maximal activity at 45 °C and was quite thermolabile, with a half-life of only 2 min at 40 °C, which may be an adaptation of E40 to the permanently cold sediment environment. The structure of E40 was solved to study its thermolability. Structural analysis showed that E40 lacks the interdomain hydrophobic interactions between loop 1 of the CAP domain and α7 of the catalytic domain compared with its thermostable homologs. Mutational analysis showed that the introduction of hydrophobic residues Trp(202) and Phe(203) in α7 significantly improved E40 stability and that a further introduction of hydrophobic residues in loop 1 made E40 more thermostable because of the formation of interdomain hydrophobic interactions. Altogether, the results indicate that the absence of interdomain hydrophobic interactions between loop 1 and α7 leads to the thermolability of E40. In addition, a comparative analysis of the structures of E40 and other thermolabile and thermostable HSLs suggests that the interdomain hydrophobic interactions between loop 1 and α7 are a key element for the thermostability of microbial HSLs. Therefore, this study not only illustrates the structural element leading to the thermolability of E40 but also reveals a structural determinant for HSL thermostability.

Protein docking using case-based reasoning.

Protein docking algorithms aim to calculate the three-dimensional (3D) structure of a protein complex starting from its unbound components. Although ab initio docking algorithms are improving, there is a growing need to use homology modeling techniques to exploit the rapidly increasing volumes of structural information that now exist. However, most current homology modeling approaches involve finding a pair of complete single-chain structures in a homologous protein complex to use as a 3D template, despite the fact that protein complexes are often formed from one or more domain-domain interactions (DDIs). To model 3D protein complexes by domain-domain homology, we have developed a case-based reasoning approach called KBDOCK which systematically identifies and reuses domain family binding sites from our database of nonredundant DDIs. When tested on 54 protein complexes from the Protein Docking Benchmark, our approach provides a near-perfect way to model single-domain protein complexes when full-homology templates are available, and it extends our ability to model more difficult cases when only partial or incomplete templates exist. These promising early results highlight the need for a new and diverse docking benchmark set, specifically designed to assess homology docking approaches.

The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15.

In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes remains also challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved the 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

Small-angle X-ray scattering studies of enzymes.

Enzyme function requires conformational changes to achieve substrate binding, domain rearrangements, and interactions with partner proteins, but these movements are difficult to observe. Small-angle X-ray scattering (SAXS) is a versatile structural technique that can probe such conformational changes under solution conditions that are physiologically relevant. Although it is generally considered a low-resolution structural technique, when used to study conformational changes as a function of time, ligand binding, or protein interactions, SAXS can provide rich insight into enzyme behavior, including subtle domain movements. In this perspective, we highlight recent uses of SAXS to probe structural enzyme changes upon ligand and partner-protein binding and discuss tools for signal deconvolution of complex protein solutions.

Unveiling the Domain-Specific and RAS Isoform-Specific Details of BRAF Regulation.

BRAF is a key member in the MAPK signaling pathway essential for cell growth, proliferation, and differentiation. Dysregulation or mutation of BRAF is often the underlying cause of various types of cancer. RAS, a small GTPase protein that acts upstream of BRAF, has been identified as a driver of up to one-third of all cancers. When BRAF interacts with RAS via the RAS binding domain (RBD) and membrane recruitment, BRAF undergoes a conformational change from an inactive, autoinhibited monomer to an active dimer and subsequently phosphorylates MEK to propagate the signal. Despite the central role of BRAF in cellular signaling, the exact order and magnitude of its activation steps has yet to be confirmed experimentally. By studying the inter- and intramolecular interactions of BRAF, we unveil the domain-specific and isoform-specific details of BRAF regulation. We employed pulldown assays, open surface plasmon resonance (OpenSPR), and hydrogen-deuterium exchange mass spectrometry (HDX-MS) to investigate the roles of the regulatory regions in BRAF activation and autoinhibition. Our results demonstrate that the BRAF specific region (BSR) and cysteine rich domain (CRD) play a crucial role in regulating the activity of BRAF. Moreover, we quantified the autoinhibitory binding affinities between the N-terminal domains and the kinase domain (KD) of BRAF and revealed the individual roles of the BRAF regulatory domains. Additionally, our findings provide evidence that the BSR negatively regulates BRAF activation in a RAS isoform-specific manner. Our findings also indicate that oncogenic BRAF-KDD594G mutant has a lower affinity for the regulatory domains, implicating that pathogenic BRAF acts through decreased propensity for autoinhibition. Collectively, our study provides valuable insights into the activation mechanism of BRAF kinase and may help to guide the development of new therapeutic strategies for cancer treatment.

Binding of Nox5's EF-Hand domain to the peptides corresponding to the phosphorylatable region and regulatory inhibitory loop in its dehydrogenase domain.

Reactive oxygen species (ROS) produced by NADPH oxidase 5 (Nox5) are regulated by Ca2+ flux through the interactions of its self-contained EF-hand domain (EFD), dehydrogenase domain (DH), and transmembrane domain. Studies suggest that the regulatory EF-hand binding domain (REFBD) and phosphorylatable (PhosR) sequences within DH play an important role in Nox5's superoxide-generating activity. However, the interplay of the EFD-DH interaction is largely unclear. Here, we used two synthetic peptides corresponding to the putative REFBD and PhosR sequences, as well as DH construct proteins, and separately studied their binding to EFD by fluorescence spectroscopy and calorimetry. With mutagenesis, we revealed that the C-terminal half domain of EFD binds specifically to REFBD in a Ca2+-dependent manner, which is driven primarily by hydrophobic interactions to form a more compact structure. On the other hand, the interaction between EFD and PhosR is not Ca2+-dependent and is primarily dominated by electrostatic interactions. The binding constants (Ka) for both peptides to EFD were calculated to be in the range of 105 M-1. The formation of the binary complex EFD/REFBD and ternary complex EFD/REFBD/PhosR was demonstrated by fluorescence resonance energy transfer (FRET). However, EFD binding to PhosR appears to be not biologically important while the conformational change on its C-terminal half domain resembles a major factor in EFD-DH domain-domain interactions.

Computational Determination of the Effects of Bacteriophage Bacteriophage Interactions in Human Body.

BACKGROUND: Chronic diseases are becoming more serious and widely spreading and this carries a heavy burden on doctors to deal with such patients. Although many of these diseases can be treated by bacteriophages, the situation is significantly dangerous in patients having concomitant more than one chronic disease, where conflicts between phages used in treating these diseases are very closer to happen. METHOD: This research paper presents a method to detecting the Bacteriophage-Bacteriophage Interaction. This method is implemented based on Domain-Domain Interactions model and it was used to infer Domain-Domain Interactions between the bacteriophages injected in the human body at the same time. RESULTS: By testing the method over bacteriophages that are used to treat tuberculosis, salmonella and virulent E.coli, many interactions have been inferred and detected between these bacteriophages. Several effects were detected for the resulted interactions such as: playing a role in DNA repair such as nonhomologous end joining, playing a role in DNA replication, playing a role in the interaction between the immune system and the tumor cells and playing a role in the stiff man syndrome. We revised all patents relating to bacteriophage bacteriophage interactions and phage therapy. CONCLUSION: The proposed method is developed to help doctors to realize the effect of simultaneously injecting different bacteriophages into the human body to treat different diseases.

Computational determination of the effects of virulent Escherichia coli and salmonella bacteriophages on human gut.

BACKGROUND AND OBJECTIVE: Salmonella and Escherichia coli are different types of bacteria that cause food poisoning in humans. In the elderly, infants and people with chronic conditions, it is very dangerous if Salmonella or E. coli gets into the bloodstream and then they must be treated by phage therapy. Treating Salmonella and E. coli by phage therapy affects the gut flora. This research paper presents a system for detecting the effects of virulent E. coli and Salmonella bacteriophages on human gut. METHODS: A method based on Domain-Domain Interactions (DDIs) model is implemented in the proposed system to determine the interactions between the proteins of human gut bacteria and the proteins of bacteriophages that infect virulent E. coli and Salmonella. The system helps gastroenterologists to realize the effect of injecting bacteriophages that infect virulent E. coli and Salmonella on the human gut. RESULTS: By testing the system over Enterobacteria phage 933W, Enterobacteria phage VT2-Sa and Enterobacteria phage P22, it resulted in four interactions between the proteins of the bacteriophages that infect E. coli O157:H7, E. coli O104:H4 and Salmonella typhimurium and the proteins of human gut bacterium strains. CONCLUSION: Several effects were detected such as: antibacterial activity against a number of bacterial species in human gut, regulation of cellular differentiation and organogenesis during gut, lung, and heart development, ammonia assimilation in bacteria, yeasts, and plants, energizing defense system and its function in the detoxification of lipopolysaccharide, and in the prevention of bacterial translocation in human gut.

Classification and Exploration of 3D Protein Domain Interactions Using Kbdock.

Comparing and classifying protein domain interactions according to their three-dimensional (3D) structures can help to understand protein structure-function and evolutionary relationships. Additionally, structural knowledge of existing domain-domain interactions can provide a useful way to find structural templates with which to model the 3D structures of unsolved protein complexes. Here we present a straightforward guide to using the "Kbdock" protein domain structure database and its associated web site for exploring and comparing protein domain-domain interactions (DDIs) and domain-peptide interactions (DPIs) at the Pfam domain family level. We also briefly explain how the Kbdock web site works, and we provide some notes and suggestions which should help to avoid some common pitfalls when working with 3D protein domain structures.