Irisin acts through its integrin receptor in a two-step process involving extracellular Hsp90α.

Exercise benefits the human body in many ways. Irisin is secreted by muscle, increased with exercise, and conveys physiological benefits, including improved cognition and resistance to neurodegeneration. Irisin acts via αV integrins; however, a mechanistic understanding of how small polypeptides like irisin can signal through integrins is poorly understood. Using mass spectrometry and cryo-EM, we demonstrate that the extracellular heat shock protein 90α (eHsp90α) is secreted by muscle with exercise and activates integrin αVß5. This allows for high-affinity irisin binding and signaling through an Hsp90α/αV/ß5 complex. By including hydrogen/deuterium exchange data, we generate and experimentally validate a 2.98 Å RMSD irisin/αVß5 complex docking model. Irisin binds very tightly to an alternative interface on αVß5 distinct from that used by known ligands. These data elucidate a non-canonical mechanism by which a small polypeptide hormone like irisin can function through an integrin receptor.

Recent advances in predicting and modeling protein-protein interactions.

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

Studying protein-protein interaction through side-chain modeling method OPUS-Mut.

Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65) and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.

The mitochondrial carrier homolog 2 is involved in down-regulation of influenza A virus replication.

BACKGROUND: The mitochondrial carrier homolog 2 (MTCH2) is a mitochondrial outer membrane protein regulating mitochondrial metabolism and functions in lipid homeostasis and apoptosis. Experimental data on the interaction of MTCH2 with viral proteins in virus-infected cells are very limited. Here, the interaction of MTCH2 with PA subunit of influenza A virus RdRp and its effects on viral replication was investigated. METHODS: The human MTCH2 protein was identified as the influenza A virus PA-related cellular factor with the Y2H assay. The interaction between GST.MTCH2 and PA protein co-expressed in transfected HEK293 cells was evaluated by GST-pull down. The effect of MTCH2 on virus replication was determined by quantification of viral transcript and/or viral proteins in the cells transfected with MTCH2-encoding plasmid or MTCH2-siRNA. An interaction model of MTCH2 and PA was predicted with protein modeling/docking algorithms. RESULTS: It was observed that PA and GST.MTCH2 proteins expressed in HEK293 cells were co-precipitated by glutathione-agarose beads. The influenza A virus replication was stimulated in HeLa cells whose MTCH2 expression was suppressed with specific siRNA, whereas the increase of MTCH2 in transiently transfected HEK293 cells inhibited viral RdRp activity. The results of a Y2H assay and protein-protein docking analysis suggested that the amino terminal part of the viral PA (nPA) can bind to the cytoplasmic domain comprising amino acid residues 253 to 282 of the MTCH2. CONCLUSION: It is suggested that the host mitochondrial MTCH2 protein is probably involved in the interaction with the viral polymerase protein PA to cause negative regulatory effect on influenza A virus replication in infected cells.

In Silico Description of the Direct Inhibition Mechanism of Endothelial Lipase by ANGPTL3.

Angiopoietin-like protein 3 (ANGPTL3) is a plasmatic protein that plays a crucial role in lipoprotein metabolism by inhibiting the lipoprotein lipase (LPL) and the endothelial lipase (EL) responsible for the hydrolysis of phospholipids on high-density lipoprotein (HDL). Interest in developing new pharmacological therapies aimed at inhibiting ANGPTL3 has been growing due to the hypolipidemic and antiatherogenic profile observed in its absence. The goal of this study was the in silico characterization of the interaction between ANGPTL3 and EL. Because of the lack of any structural information on both the trimeric coiled-coil N-terminal domain of ANGPTL3 and the EL homodimer as well as data regarding their interactions, the first step was to obtain the three-dimensional model of these two proteins. The models were then refined via molecular dynamics (MD) simulations and used to investigate the interaction mechanism. The analysis of interactions in different docking poses and their refinement via MD allowed the identification of three specific glutamates of ANGPTL3 that recognize a positively charged patch on the surface of EL. These ANGPTL3 key residues, i.e., Glu154, Glu157, and Glu160, could form a putative molecular recognition site for EL. This study paves the way for future investigations aimed at confirming the recognition site and at designing novel inhibitors of ANGPTL3.

Structural modeling of peptide toxin-ion channel interactions using RosettaDock.

Voltage-gated ion channels play essential physiological roles in action potential generation and propagation. Peptidic toxins from animal venoms target ion channels and provide useful scaffolds for the rational design of novel channel modulators with enhanced potency and subtype selectivity. Despite recent progress in obtaining experimental structures of peptide toxin-ion channel complexes, structural determination of peptide toxins bound to ion channels in physiologically important states remains challenging. Here we describe an application of RosettaDock approach to the structural modeling of peptide toxins interactions with ion channels. We tested this approach on 10 structures of peptide toxin-ion channel complexes and demonstrated that it can sample near-native structures in all tested cases. Our approach will be useful for improving the understanding of the molecular mechanism of natural peptide toxin modulation of ion channel gating and for the structural modeling of novel peptide-based ion channel modulators.

Mapping of antibody epitopes based on docking and homology modeling.

Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody-antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.

IsAb: a computational protocol for antibody design.

The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.

Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.

Protein-protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein-protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein-protein interactions. Recently, deep learning has led to a breakthrough in intra-protein contact prediction, achieving an unusual high accuracy in recent Critical Assessment of protein Structure Prediction (CASP) structure prediction challenges. However, due to the limited number of known homologous protein-protein interactions and the challenge to generate joint multiple sequence alignments of two interacting proteins, the advances in inter-protein contact prediction remain limited. Here, we have proposed a deep learning model to predict inter-protein residue-residue contacts across homo-oligomeric protein interfaces, named as DeepHomo. Unlike previous deep learning approaches, we integrated intra-protein distance map and inter-protein docking pattern, in addition to evolutionary coupling, sequence conservation, and physico-chemical information of monomers. DeepHomo was extensively tested on both experimentally determined structures and realistic CASP-Critical Assessment of Predicted Interaction (CAPRI) targets. It was shown that DeepHomo achieved a high precision of >60% for the top predicted contact and outperformed state-of-the-art direct-coupling analysis and machine learning-based approaches. Integrating predicted inter-chain contacts into protein-protein docking significantly improved the docking accuracy on the benchmark dataset of realistic homo-dimeric targets from CASP-CAPRI experiments. DeepHomo is available at http://huanglab.phys.hust.edu.cn/DeepHomo/.

Structural analysis and inhibition capacity of dioscorin protein yam: Theoretical study by molecular docking and dynamic simulations.

Proteases such as trypsins in the gut of Spodoptera frugiperda are responsible for breaking down dietary proteins into amino acids necessary for insect growth and development. In this study, we characterized the insecticidal potential of dioscorin, the storage protein of yam (Dioscorea alata), using molecular docking and molecular dynamics simulations to determine the interactions between trypsin enzymes and the protein inhibitor dioscorin. To achieve this, we used the three-dimensional structures of the trypsin-like digestive enzymes of S. frugiperda, a pest of corn and cotton, as receptors or target molecules. We performed protein-protein docking using Cluspro software, estimation of the binding free energy, and information on the dynamic and time-dependent behavior of dioscorin-trypsin complexes using the NAMD package. Our computational analysis showed that dioscorin can bind to the digestive trypsins of S. frugiperda, as confirmed by the affinity energy values (-1022.4 to -1236.9), stability of the complexes during the simulation trajectory, and binding free energy values between -57.3 and -66.9 kcal/mol. Additionally, dioscorin uses two reactive sites to bind trypsin, but the largest contribution to the interaction energy is made by amino acid residues between amino acid backbone positions 8-14 by hydrogen bonds, hydrophobic, and Van der Waals (VdW) interactions. VdW is the energy that makes the greatest contribution to the binding energy. Collectively, our findings demonstrate, for the first time, the binding capacity of the yam protein dioscorin to the digestive trypsin of S. frugiperda. These promising results suggest a possible bioinsecticide action of dioscorin.

Is the BNT162b2 vaccine still effective against the latest variant: XBB.1.5?

Background: The XBB.1.5 sub-variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron now continues to spread rapidly due to the increased transmission rate as a result of increased affinity of the virus binding over the ACE-2 receptor - a gained property due to the mutation that occurred in spike protein. Aim: The protectivity of BNT162b2 antibodies produced in the serum of patients is an important parameter for preventing transmission. However, the affinity of the antibodies of patients vaccinated with BNT162b2 over the latest SARS-CoV-2 variant, XBB.1.5, is not well established. This study aimed to evaluate the efficacy of the BNT162b2 vaccine-induced antibody on XBB.1.5 by comparing the X-ray crystallographic structures and spike protein mutations of BA.5 and XBB.1.5 using in silico methods. Materials and Methods: Binding points and binding affinity values of the BNT162b2 antibody with BA.5 and XBB.1.5 spike protein were calculated using ClusPro 2.0 protein-protein docking and Discovery Studio 2021 Client software. Mutations in the genetic code of the spike protein for SARS-CoV-2 BA.5 and XBB.1.5 sub-variants were screened using the GISAID database. Results: Binding affinity values showed that BNT162b2 had higher negative values in the XBB.1.5 sub-variant than BA.5 at the mutation sites at the binding region. The results suggested that BNT162b2 may retain its activity despite mutations and conformational changes in the binding site of the XBB.1.5. Conclusion: The findings of this study shed light on the importance and usability of the current BNT162b2 vaccine for XBB.1.5 and future variants of concern.

Identification of a druggable protein-protein interaction site between mutant p53 and its stabilizing chaperone DNAJA1.

The TP53 gene is the most frequently mutated gene in human cancers, and the majority of TP53 mutations are missense mutations. As a result, these mutant p53 (mutp53) either directly lose wildtype p53 (wtp53) tumor suppressor function or exhibit a dominant negative effect over wtp53. In addition, some mutp53 have acquired new oncogenic function (gain of function). Therefore, targeting mutp53 for its degradation may serve as a promising strategy for cancer prevention and therapy. Based on our previous finding that farnesylated DNAJA1 is a crucial chaperone in maintaining mutp53 stabilization, and by using an in silico approach, we built 3D homology models of human DNAJA1 and mutp53R175H proteins, identified the interacting pocket in the DNAJA1-mutp53R175H complex, and found one critical druggable small molecule binding site in the DNAJA1 glycine/phenylalanine-rich region. We confirmed that the interacting pocket in the DNAJA1-mutp53R175H complex was crucial for stabilizing mutp53R175H using a site-directed mutagenesis approach. We further screened a drug-like library to identify a promising small molecule hit (GY1-22) against the interacting pocket in the DNAJA1-mutp53R175H complex. The GY1-22 compound displayed an effective activity against the DNAJA1-mutp53R175H complex. Treatment with GY1-22 significantly reduced mutp53 protein levels, enhanced Waf1p21 expression, suppressed cyclin D1 expression, and inhibited mutp53-driven pancreatic cancer growth both in vitro and in vivo. Together, our results indicate that the interacting pocket in the DNAJA1-mutp53R175H complex is critical for mutp53's stability and oncogenic function, and DNAJA1 is a robust therapeutic target for developing the efficient small molecule inhibitors against oncogenic mutp53.

Exploring CCRL2 chemerin binding using accelerated molecular dynamics.

Chemokine (C-C motif) receptor-like 2 (CCRL2), is a seven transmembrane receptor closely related to the chemokine receptors CCR1, CCR2, CCR3, and CCR5. Nevertheless, CCRL2 is unable to activate conventional G-protein dependent signaling and to induce cell directional migration. The only commonly accepted CCRL2 ligand is the nonchemokine chemotactic protein chemerin (RARRES2). The chemerin binding to CCLR2 does induce leukocyte chemotaxis, yet, genetic targeting of CCRL2 was shown to modulate the inflammatory response in different experimental models. This mechanism was shown to be crucial for lung dendritic cell migration, neutrophil recruitment, and Natural Killer cell-dependent immune surveillance in lung cancer. To gain more insight in the interactions involved in the CCRL2-chemerin, the binding complexes were generated by protein-protein docking, then submitted to accelerated molecular dynamics. The obtained trajectories were inspected by principal component analyses followed by kernel density estimation to identify the ligand-receptor regions most frequently involved in the binding. To conclude, the reported analyses led to the identification of the putative hot-spot residues involved in CCRL2-chemerin binding.

Binding mode of brazzein to the taste receptor based on crystal structure and docking simulation.

Several natural substances including protein produce sweet taste. Brazzein, derived from the plant Pentadipladra brazzeana, is one of the sweet proteins that bind to the taste receptor with stronger sweetness than sugar. Mutations of this protein affect its flavour, yielding higher sweetness in D29K and lower sweetness in R43A. To elucidate its sweet mechanism in the taste receptor, we determined the structures of two variants, D29K and R43A, to a resolution of 1.5 Å and 1.3 Å, respectively. Structures of the brazzein exhibit two α-helix and three ß-sheets connected by four disulfide bonds with a significantly altered electrostatic distribution on the surface. Using the high-resolution structure data and models of the taste receptors T1R2 and T1R3 in the AlphaFold Protein Structure Database, we performed a docking calculation on the receptors and report that brazzein is bound between the two cysteine rich domains (CRDs) of the heterodimer protein complex. Substitution to lysine in D29K resulted in an increased number of hydrogen bonds in the T1R2 receptor, while substitution to alanine in R43A ablated a polar interaction in the T1R3 receptor. The significantly altered interaction of the variants at the interface is consistent with a change of the sweetness. The high-resolution structure and the docking model in this study may provide a structural basis to understand the flavour mechanism induced by the sweet protein.

Modulation of cardiac thin filament structure by phosphorylated troponin-I analyzed by protein-protein docking and molecular dynamics simulation.

Tropomyosin, controlled by troponin-linked Ca2+-binding, regulates muscle contraction by a macromolecular scale steric-mechanism that governs myosin-crossbridge-actin interactions. At low-Ca2+, C-terminal domains of troponin-I (TnI) trap tropomyosin in a position on thin filaments that interferes with myosin-binding, thus causing muscle relaxation. Steric inhibition is reversed at high-Ca2+ when TnI releases from F-actin-tropomyosin as Ca2+ and the TnI switch-peptide bind to the N-lobe of troponin-C (TnC). The opposite end of cardiac TnI contains a phosphorylation-sensitive ∼30 residue-long N-terminal peptide that is absent in skeletal muscle, and likely modifies these interactions in hearts. Here, PKA-dependent phosphorylation of serine 23 and 24 modulates Ca2+ and possibly switch-peptide binding to TnC, causing faster relaxation during the cardiac-cycle (lusitropy). The cardiac-specific N-terminal TnI domain is not captured in crystal structures of troponin or in cryo-EM reconstructions of thin filaments; thus, its global impact on thin filament structure and function is uncertain. Here, we used protein-protein docking and molecular dynamics simulation-based protocols to build a troponin model that was guided by and hence consistent with the recent seminal Yamada structure of Ca2+-activated thin filaments. We find that when present on thin filaments, phosphorylated Ser23/24 along with adjacent polar TnI residues interact closely with both tropomyosin and the N-lobe of TnC during our simulations. These interactions would likely bias tropomyosin to an off-state positioning on actin. In situ, such enhanced relaxation kinetics would promote cardiac lusitropy.

Genome-Wide Analysis of Wheat GATA Transcription Factor Genes Reveals Their Molecular Evolutionary Characteristics and Involvement in Salt and Drought Tolerance.

GATA transcription factor genes participate in plant growth, development, morphogenesis, and stress response. In this study, we carried out a comprehensive genome-wide analysis of wheat GATA transcription factor genes to reveal their molecular evolutionary characteristics and involvement in salt and drought tolerance. In total, 79 TaGATA genes containing a conserved GATA domain were identified in the wheat genome, which were classified into four subfamilies. Collinear analysis indicated that fragment duplication plays an important role in the amplification of the wheat GATA gene family. Functional disproportionation analysis between subfamilies found that both type I and type II functional divergence simultaneously occurs in wheat GATA genes, which might result in functional differentiation of the TaGATA gene family. Transcriptional expression analysis showed that TaGATA genes generally have a high expression level in leaves and in response to drought and salt stresses. Overexpression of TaGATA62 and TaGATA73 genes significantly enhanced the drought and salt tolerance of yeast and Arabidopsis. Protein-protein docking indicated that TaGATAs can enhance drought and salt tolerance by interacting between the DNA-binding motif of GATA transcription factors and photomorphogenesis-related protein TaCOP9-5A. Our results provided a base for further understanding the molecular evolution and functional characterization of the plant GATA gene family in response to abiotic stresses.

Elucidating the Interaction between Pyridoxine 5'-Phosphate Oxidase and Dopa Decarboxylase: Activation of B6-Dependent Enzyme.

Pyridoxal 5'-phosphate (PLP), the active form of vitamin B6, serves as a cofactor for scores of B6-dependent (PLP-dependent) enzymes involved in many cellular processes. One such B6 enzyme is dopa decarboxylase (DDC), which is required for the biosynthesis of key neurotransmitters, e.g., dopamine and serotonin. PLP-dependent enzymes are biosynthesized as apo-B6 enzymes and then converted to the catalytically active holo-B6 enzymes by Schiff base formation between the aldehyde of PLP and an active site lysine of the protein. In eukaryotes, PLP is made available to the B6 enzymes through the activity of the B6-salvage enzymes, pyridoxine 5'-phosphate oxidase (PNPO) and pyridoxal kinase (PLK). To minimize toxicity, the cell keeps the content of free PLP (unbound) very low through dephosphorylation and PLP feedback inhibition of PNPO and PLK. This has led to a proposed mechanism of complex formation between the B6-salvage enzymes and apo-B6 enzymes prior to the transfer of PLP, although such complexes are yet to be characterized at the atomic level, presumably due to their transient nature. A computational study, for the first time, was used to predict a likely PNPO and DDC complex, which suggested contact between the allosteric PLP tight-binding site on PNPO and the active site of DDC. Using isothermal calorimetry and/or surface plasmon resonance, we also show that PNPO binds both apoDDC and holoDDC with dissociation constants of 0.93 ± 0.07 µM and 2.59 ± 0.11 µM, respectively. Finally, in the presence of apoDDC, the tightly bound PLP on PNPO is transferred to apoDDC, resulting in the formation of about 35% holoDDC.

The First Snake Venom KTS/Disintegrins-Integrin Interactions Using Bioinformatics Approaches.

Snake venom contains a number of active molecules that have been shown to possess high anti-tumor activities; disintegrins are an excellent example among these. Their ability to interact and bind with integrins suggests that they could be very valuable molecules for the development of new cancer therapeutic approaches. However, in the absence of a clear Lysine-Threonine-Serine (KTS) Disintegrins Integrin interaction model, the exact compound features behind it are still unknown. In this study, we investigated the structural characteristics of three KTS-disintegrins and the interaction mechanisms with the α1ß1 integrin receptor using in silico bioinformatics approaches. Normal mode analysis showed that the flexibility of the KTSR motif and the C-terminal region play a key role and influence the KTS-Disintegrin-integrin interaction. Protein-protein docking also suggested that the interaction involving the KTSR motif is highly dependent on the residue following K21, S23 and R24. These findings contribute to a better understanding of the KTS-Disintegrin-Integrin structural differences and their interactions with α1ß1 receptors, which could improve the selection process of the best active molecules for antitumor therapies.

A computational framework for modeling functional protein-protein interactions.

Protein interactions and their assemblies assist in understanding the cellular mechanisms through the knowledge of interactome. Despite recent advances, a vast number of interacting protein complexes is not annotated by three-dimensional structures. Therefore, a computational framework is a suitable alternative to fill the large gap between identified interactions and the interactions with known structures. In this work, we develop an automated computational framework for modeling functionally related protein-complex structures utilizing GO-based semantic similarity technique and co-evolutionary information of the interaction sites. The framework can consider protein sequence and structure information as input and employ both rigid-body docking and template-based modeling exploiting the existing structural templates and sequence homology information from the PDB. Our framework combines geometric as well as physicochemical features for re-ranking the docking decoys. The proposed framework has an 83% success rate when tested on a benchmark dataset while considering Top1 models for template-based modeling and Top10 models for the docking pipeline. We believe that our computational framework can be used for any pair of proteins with higher confidence to identify the functional protein-protein interactions.

Protein oligomer structure prediction using GALAXY in CASP14.

Proteins perform their functions by interacting with other biomolecules. For these interactions, proteins often form homo- or hetero-oligomers as well. Thus, oligomer protein structures provide important clues regarding the biological roles of proteins. To this end, computational prediction of oligomer structures may be a useful tool in the absence of experimentally resolved structures. Here, we describe our server and human-expert methods used to predict oligomer structures in the CASP14 experiment. Examples are provided for cases in which manual domain-splitting led to improved oligomeric domain structures by ab initio docking, automated oligomer structure refinement led to improved subunit orientation and terminal structure, and manual oligomer modeling utilizing literature information generated a reasonable oligomer model. We also discussed the results of post-prediction docking calculations with AlphaFold2 monomers as input in comparison to our blind prediction results. Overall, ab initio docking of AlphaFold2 models did not lead to better oligomer structure prediction, which may be attributed to the interfacial structural difference between the AlphaFold2 monomer structures and the crystal oligomer structures. This result poses a next-stage challenge in oligomer structure prediction after the success of AlphaFold2. For successful protein assembly structure prediction, a different approach that exploits further evolutionary information on the interface and/or flexible docking taking the interfacial conformational flexibilities of subunit structures into account is needed.