An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Integrative Modeling in the Age of Machine Learning: A Summary of HADDOCK Strategies in CAPRI Rounds 47-55

The HADDOCK team participated in CAPRI rounds 47-55 as server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.

Advancements in nanobody generation: Integrating conventional, in silico, and machine learning approaches

Nanobodies, derived from camelids and sharks, offer compact, single-variable heavy-chain antibodies with diverse biomedical potential. This review explores their generation methods, including display techniques on phages, yeast, or bacteria, and computational methodologies. Integrating experimental and computational approaches enhances understanding of nanobody structure and function. Future trends involve leveraging next-generation sequencing, machine learning, and artificial intelligence for efficient candidate selection and predictive modeling. The convergence of traditional and computational methods promises revolutionary advancements in precision biomedical applications such as targeted drug delivery and diagnostics. Embracing these technologies accelerates nanobody development, driving transformative breakthroughs in biomedicine and paving the way for precision medicine and biomedical innovation.

Molecular simulation reveals that pathogenic mutations in BTB/ANK domains of Arabidopsis thaliana NPR1 circumscribe the EDS1-mediated immune regulation

The NPR1 (nonexpressor of pathogenesis-related genes 1) is a key regulator of the salicylic-acid-mediated immune response caused by pathogens in Arabidopsis thaliana. Mutations C150Y and H334Y in the BTB/ANK domains of NPR1 inhibit the defense response, and transcriptional co-activity with enhanced disease susceptibility 1 (EDS1) has been revealed experimentally. This study examined the conformational changes and reduced NPR1-EDS1 interaction upon mutation using a molecular dynamics simulation. Initially, BTBC150YNPR1 and ANKH334YNPR1 were categorized as pathological mutations rather than others based on sequence conservation. A distant ortholog was used to map the common residues shared among the wild-type because the mutations were highly conserved. Overall, 179 of 373 residues were determining the secondary structures and fold versatility of conformations. In addition, the mutational hotspots Cys150, Asp152, Glu153, Cys155, His157, Cys160, His334, Arg339 and Lys370 were crucial for oligomer-to-monomer exchange. Subsequently, the atomistic simulations with free energy (MM/PB(GB)SA) calculations predicted structural displacements engaging in the N-termini α5133-178α7 linker connecting the central ANK regions (α13260-290α14 and α18320-390α22), where prominent long helices (α516) and short helices (α310) replaced with β-turns and loops disrupting hydrogen bonds and salt bridges in both mutants implicating functional regulation and activation. Furthermore, the mutation repositions the intact stability of multiple regions (L13C149-N356α20BTB/ANK-α17W301-E357α21N-ter/coiled-coil) compromising a dynamic interaction of NPR1-EDS1. By unveiling the transitions between the distinct functions of mutational perception, this study paves the way for future investigation to orchestrate additive host-adapted transcriptional reprogramming that controls defense-related regulatory mechanisms of NPR1s in plants.

The structural principles underlying molybdenum insertase complex assembly

Within the cell, the trace element molybdenum (Mo) is only biologically active when complexed either within the nitrogenase-specific FeMo cofactor or within the molybdenum cofactor (Moco). Moco consists of an organic part, called molybdopterin (MPT) and an inorganic part, that is, the Mo-center. The enzyme which catalyzes the Mo-center formation is the molybdenum insertase (Mo-insertase). Mo-insertases consist of two functional domains called G- and E-domain. The G-domain catalyzes the formation of adenylated MPT (MPT-AMP), which is the substrate for the E-domain, that catalyzes the actual molybdate insertion reaction. Though the functions of E- and G-domain have been elucidated to great structural and mechanistic detail, their combined function is poorly characterized. In this work, we describe a structural model of the eukaryotic Mo-insertase Cnx1 complex that was generated based on cross-linking mass spectrometry combined with computational modeling. We revealed Cnx1 to form an asymmetric hexameric complex which allows the E- and G-domain active sites to align in a catalytic productive orientation toward each other.

Integrative modeling of diverse protein-peptide systems using CABS-dock

The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors.

Transmission of SARS-CoV-2 from humans to animals and potential host adaptation

SARS-CoV-2, the causative agent of the COVID-19 pandemic, can infect a wide range of mammals. Since its spread in humans, secondary host jumps of SARS-CoV-2 from humans to multiple domestic and wild populations of mammals have been documented. Understanding the extent of adaptation to these animal hosts is critical for assessing the threat that the spillback of animal-adapted SARS-CoV-2 into humans poses. We compare the genomic landscapes of SARS-CoV-2 isolated from animal species to that in humans, profiling the mutational biases indicative of potentially different selective pressures in animals. We focus on viral genomes isolated from mink (Neovison vison) and white-tailed deer (Odocoileus virginianus) for which multiple independent outbreaks driven by onward animal-to-animal transmission have been reported. We identify five candidate mutations for animal-specific adaptation in mink (NSP9_G37E, Spike_F486L, Spike_N501T, Spike_Y453F, ORF3a_L219V), and one in deer (NSP3a_L1035F), though they appear to confer a minimal advantage for human-to-human transmission. No considerable changes to the mutation rate or evolutionary trajectory of SARS-CoV-2 has resulted from circulation in mink and deer thus far. Our findings suggest that minimal adaptation was required for onward transmission in mink and deer following human-to-animal spillover, highlighting the 'generalist' nature of SARS-CoV-2 as a mammalian pathogen.

SARS-CoV-2 spike evolutionary behaviors; simulation of N501Y mutation outcomes in terms of immunogenicity and structural characteristic

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), a large number of mutations in its genome have been reported. Some of the mutations occur in noncoding regions without affecting the pathobiology of the virus, while mutations in coding regions are significant. One of the regions where a mutation can occur, affecting the function of the virus is at the receptor‐binding domain (RBD) of the spike protein. RBD interacts with angiotensin‐converting enzyme 2 (ACE2) and facilitates the entry of the virus into the host cells. There is a lot of focus on RBD mutations, especially the displacement of N501Y which is observed in the UK/Kent, South Africa, and Brazilian lineages of SARS‐CoV‐2. Our group utilizes computational biology approaches such as immunoinformatics, protein–protein interaction analysis, molecular dynamics, free energy computation, and tertiary structure analysis to disclose the consequences of N501Y mutation at the molecular level. Surprisingly, we discovered that this mutation reduces the immunogenicity of the spike protein; also, displacement of Asn with Tyr reduces protein compactness and significantly increases the stability of the spike protein and its affinity to ACE2. Moreover, following the N501Y mutation secondary structure and folding of the spike protein changed dramatically.

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

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

Evaluation of CONSRANK-Like Scoring Functions for Rescoring Ensembles of Protein–Protein Docking Poses

Scoring is a challenging step in protein–protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface size is critical for successful rescoring in this context, but that consensus rescoring in itself performs less well than traditional physics-based evaluation. The results of physics-based and consensus-based rescoring are partially overlapping, supporting the use of a combination of these approaches.