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Abstract Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness.

Abstract Cryo-EM has emerged as the most important technique for structure determination of macromolecular complexes. However, raw cryo-EM maps often exhibit loss of contrast at high resolution and heterogeneity over the entire map. As such, various post-processing methods have been proposed to improve cryo-EM maps. Nevertheless, it is still challenging to improve both the quality and interpretability of EM maps. Addressing the challenge, we present a three-dimensional Swin-Conv-UNet-based deep learning framework to improve cryo-EM maps, named EMReady, by not only implementing both local and non-local modeling modules in a multiscale UNet architecture but also simultaneously minimizing the local smooth L1 distance and maximizing the non-local structural similarity between processed experimental and simulated target maps in the loss function. EMReady was extensively evaluated on diverse test sets of 110 primary cryo-EM maps and 25 pairs of half-maps at 3.0–6.0 Å resolutions, and compared with five state-of-the-art map post-processing methods. It is shown that EMReady can not only robustly enhance the quality of cryo-EM maps in terms of map-model correlations, but also improve the interpretability of the maps in automatic de novo model building.

One challenge in cryo-EM is to build atomic models into intermediate resolution maps. Here, the authors present a deep learning-guided iterative assembling method by integrating AlphaFold, FFTbased fitting, and domain-based refinement.

Abstract Cyclic peptides formed by disulfide bonds have been one large group of common drug candidates in drug development. Structural information of a peptide is essential to understand its interaction with its target. However, due to the high flexibility of peptides, it is difficult to sample the near-native conformations of a peptide. Here, we have developed an extended version of our MODPEP approach, named MODPEP2.0, to fast generate the conformations of cyclic peptides formed by a disulfide bond. MODPEP2.0 builds the three-dimensional (3D) structures of a cyclic peptide from scratch by assembling amino acids one by one onto the cyclic fragment based on the constructed rotamer and cyclic backbone libraries. Being tested on a data set of 193 diverse cyclic peptides, MODPEP2.0 obtained a considerable advantage in both accuracy and computational efficiency, compared with other sampling algorithms including PEP-FOLD, ETKDG, and modified ETKDG (mETKDG). MODPEP2.0 achieved a high sampling accuracy with an average C $$\alpha$$ α RMSD of 2.20 Å and 1.66 Å when 10 and 100 conformations were considered, respectively, compared with 3.41 Å and 2.62 Å for PEP-FOLD, 3.44 Å and 3.16 Å for ETKDG, 3.09 Å and 2.72 Å for mETKDG. MODPEP2.0 also reproduced experimental peptide structures for 81.35% of the test cases when an ensemble of 100 conformations were considered, compared with 54.95%, 37.50% and 50.00% for PEP-FOLD, ETKDG, and mETKDG. MODPEP2.0 is computationally efficient and can generate 100 peptide conformations in one second. MODPEP2.0 will be useful in sampling cyclic peptide structures and modeling related protein-peptide interactions, facilitating the development of cyclic peptide drugs.

Abstract Background Protein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches. Results We have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1. Conclusions The present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server at http://hdock.phys.hust.edu.cn/.

Symmetric proteins play important roles in many biological processes, such as signal transduction and molecular transportation. Therefore, determining the symmetric oligomeric structure of subunits is crucial to investigate the molecular mechanism of the related processes. Due to the high cost and technical difficulties associated with many experimental methods, computational approaches, such as molecular docking, have played an important complementary role in the determination of symmetric complex structures, in which a benchmark data set is pressingly needed. In the present work, we develop a comprehensive and non-redundant benchmark for symmetric protein docking based on the structures in the Protein Data Bank (PDB). The diverse dataset consists of 251 targets, including 212 cases with cyclic groups symmetry, 35 cases with dihedral groups symmetry, 3 cases with cubic groups symmetry, and 1 case with helical symmetry. According to the conformational changes in the interface between bound and unbound structures, the 251 targets were classified into three groups: 176 "easy", 37 "medium", and 38 "difficult" cases. A preliminary docking test on the targets of cyclic groups symmetry with M-ZDOCK indicated that symmetric multimer docking remains challenging. The benchmark will be beneficial for the development of symmetric protein docking algorithms. The proposed benchmark data set is available for download at http://huanglab.phys.hust.edu.cn/SDBenchmark/.

Abstract Conformation generation of protein-bound peptides is critical for the determination of protein–peptide complex structures. Despite significant progress in conformer generation of small molecules, few methods have been developed for modeling protein-bound peptide conformations. Here, we have developed a fast de novo peptide modeling algorithm, referred to as MODPEP, for conformational sampling of protein-bound peptides. Given a sequence, MODPEP builds the peptide 3D structure from scratch by assembling amino acids or helix fragments based on constructed rotamer and helix libraries. The MODPEP algorithm was tested on a diverse set of 910 experimentally determined protein-bound peptides with 3–30 amino acids from the PDB and obtained an average accuracy of 1.90 Å when 200 conformations were sampled for each peptide. On average, MODPEP obtained a success rate of 74.3% for all the 910 peptides and ≥ 90% for short peptides with 3–10 amino acids in reproducing experimental protein-bound structures. Comparative evaluations of MODPEP with three other conformer generation methods, PEP-FOLD3, RDKit, and Balloon, have also been performed in both accuracy and success rate. MODPEP is fast and can generate 100 conformations for less than one second. The fast MODPEP will be beneficial for large-scale de novo modeling and docking of peptides. The MODPEP program and libraries are available for download at http://huanglab.phys.hust.edu.cn/ .

The outbreak of a novel coronavirus, which was later formally named the severe acute respiratory coronavirus 2 (SARS-CoV-2), has caused a worldwide public health crisis. Previous studies showed that SARS-CoV-2 is highly homologous to SARS-CoV and infects humans through the binding of the spike protein to ACE2. Here, we have systematically studied the molecular mechanisms of human infection with SARS-CoV-2 and SARS-CoV by protein-protein docking and MD simulations. It was found that SARS-CoV-2 binds ACE2 with a higher affinity than SARS-CoV, which may partly explain that SARS-CoV-2 is much more infectious than SARS-CoV. In addition, the spike protein of SARS-CoV-2 has a significantly lower free energy than that of SARS-CoV, suggesting that SARS-CoV-2 is more stable and may survive a higher temperature than SARS-CoV. This provides insights into the evolution of SARS-CoV-2 because SARS-like coronaviruses have originated in bats. Our computation also suggested that the RBD-ACE2 binding for SARS-CoV-2 is much more temperature-sensitive than that for SARS-CoV. Thus, it is expected that SARS-CoV-2 would decrease its infection ability much faster than SARS-CoV when the temperature rises. These findings would be beneficial for the disease prevention and drug/vaccine development of SARS-CoV-2.

The recruitment and transference of proteins through protein−protein interactions is a general process involved in various biological functions in cells. Despite the importance of this general process, the dynamic mechanism of how proteins are recruited and transferred from one interacting partner to another remains unclear. In this study, we investigated the dynamic mechanisms of recruitment and translocation of histone chaperone CIA/ASF1 for nucleosome disassembly by exploring the conformational space and the free energy profile of unbound DBD(CCG1) and CIA/ASF1-bound DBD(CCG1) systems through extensive molecular dynamics simulations. It was found that there exists three metastable conformational states for DBD(CCG1), an unbound closed state, a CIA/ASF1-bound half-open state, and an open state. The free energy landscape shows that the closed state and the half-open state are separated by a high free energy barrier, while the half-open state and the open state are connected with a moderate free energy increase. The high free energy barrier between the closed and half-open states explains why DBD(CCG1) can recruit CIA/ASF1 and remain in the binding state during the transportation. In addition, the asymmetric binding of CIA/ASF1 on DBD(CCG1) allows DBD(CCG1) to adopt the open state by moving one of its two domains, such that the exposed domain of DBD(CCG1) is able to recognize the acetylated histone H4 tails. As such, CIA/ASF1 has a chance to translocate from DBD(CCG1) to histone, which is also facilitated by the moderate energy increase from the bound half-open state to the open state of DBD(CCG1). These findings suggest that the recruitment and transference of histone chaperone CIA/ASF1 is highly favored by its interaction with DBD(CCG1) via conformational selection and asymmetric binding, which may represent a general mechanism of similar biological processes.

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion.