Toripalimab Plus Bevacizumab as First-line Treatment for Advanced Hepatocellular Carcinoma: A Prospective, Multicenter, Single-Arm, Phase II Trial.

PURPOSE: This phase II, multicenter, prospective, single-arm study aimed to evaluate the efficacy and safety of toripalimab plus bevacizumab for treating advanced hepatocellular carcinoma (HCC). PATIENTS AND METHODS: Treatment-naïve patients with advanced HCC received toripalimab 240 mg plus bevacizumab 15 mg/kg every 3 weeks. The primary endpoints included safety and tolerability and objective response rate (ORR) assessed by the investigator per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. RESULTS: Fifty-four patients were enrolled between April 17, 2020, and December 11, 2020. As assessed by the investigator according to RECIST v1.1, the ORR was 31.5% [95% confidence interval (CI), 19.5-45.6] and the lower bound of the 95% CI was above the prespecified boundary of 10%. The independent review committee (IRC) assessed ORR according to the modified RECIST (mRECIST), which was 46.3% (95% CI, 32.6-60.4). The median progression-free survival was 8.5 (95% CI, 5.5-11.0) and 9.8 months (95% CI, 5.6 to not evaluable) as assessed by the investigator according to RECIST v1.1 and IRC according to mRECIST criteria, respectively. The median overall survival (OS) was not reached, and the 12- and 24-month OS rates were 77.3% and 63.5%, respectively. Grade 3 or higher treatment-emergent adverse events (TEAEs) occurred in 27 patients (50.0%). The most common TEAEs were proteinuria (59.3%), hypertension (38.9%), increased aspartate aminotransferase (33.3%), increased amylase (29.6%), decreased platelet count (27.8%), and increased bilirubin levels (27.8%). CONCLUSIONS: Toripalimab plus bevacizumab showed a favorable efficacy and safety profile, supporting further studies on this combination regimen as a first-line treatment for advanced HCC.

ADSC-Exos enhance functional recovery after spinal cord injury by inhibiting ferroptosis and promoting the survival and function of endothelial cells through the NRF2/SLC7A11/GPX4 pathway.

BACKGROUND: Spinal cord injury (SCI) is a devastating disease that causes major motor, sensory and autonomic dysfunctions. Currently, there is a lack of effective treatment. In this study, we aimed to investigate the potential mechanisms of Exosomes from adipose-derived stem cells (ADSC-Exos) in reducing ferroptosis and promoting angiogenesis after spinal cord injury. METHODS: We isolated ADSC-Exos, the characteristics of which were confirmed. In vitro, we tested the potential of ADSC-Exos to promote the survival and function of human brain microvascular endothelial cells (HBMECs) and analyzed the ferroptosis of HBMECs. In vivo, we established rat models of SCI and locally injected ADSC-Exos to verify their efficacy. RESULTS: ADSC-Exos can reduce reactive oxygen species (ROS) accumulation and cell damage induced by an excessive inflammatory response in HBMECs. ADSC-Exos inhibit ferroptosis induced by excessive inflammation and upregulate the expression of glutathione peroxidase 4(GPX4) in HBMECs. It can also effectively promote proliferation, migration, and vessel-like structure formation. In vitro, ADSC-Exos improved behavioral function after SCI and increased the number and density of blood vessels around the damaged spinal cord. Moreover, we found that ADSC-Exos could increase nuclear factor erythroid-2-related factor 2(NRF2) expression and nuclear translocation, thereby affecting the expression of solute carrier family 7 member 11(SLC7A11) and GPX4, and the NRF2 inhibitor ML385 could reverse the above changes. CONCLUSION: Our results suggest that ADSC-Exos may inhibit ferroptosis and promote the recovery of vascular and neural functions after SCI through the NRF2/SLC7A11/GPX4 pathway. This may be a potential therapeutic mechanism for spinal cord injury.

Computational simulations of bispecific T cell engagers by a multiscale model.

The use of bispecific antibodies as T cell engagers can bypass the normal T cell receptor-major histocompatibility class interaction, redirect the cytotoxic activity of T cells, and lead to highly efficient tumor cell killing. However, this immunotherapy also causes significant on-target off-tumor toxicologic effects, especially when it is used to treat solid tumors. To avoid these adverse events, it is necessary to understand the fundamental mechanisms involved in the physical process of T cell engagement. We developed a multiscale computational framework to reach this goal. The framework combines simulations on the intercellular and multicellular levels. On the intercellular level, we simulated the spatial-temporal dynamics of three-body interactions among bispecific antibodies, CD3 and tumor-associated antigens (TAAs). The derived number of intercellular bonds formed between CD3 and TAAs was further transferred to the multicellular simulations as the input parameter of adhesive density between cells. Through the simulations under various molecular and cellular conditions, we were able to gain new insights into how to adopt the most appropriate strategy to maximize the drug efficacy and avoid the off-target effect. For instance, we discovered that the low antibody-binding affinity resulted in the formation of large clusters at the cell-cell interface, which could be important to control the downstream signaling pathways. We also tested different molecular architectures of the bispecific antibody and suggested the existence of an optimal length in regulating the T cell engagement. Overall, the current multiscale simulations serve as a proof-of-concept study to help in the future design of new biological therapeutics.

Understanding the General Principles of T Cell Engagement by Multiscale Computational Simulations.

The use of bispecific antibodies as T cell engagers can bypass the normal TCR-MHC interaction, redirect the cytotoxic activity of T-cells, and lead to highly efficient tumor cell killing. However, this immunotherapy also causes significant on-target off-tumor toxicologic effects, especially when they were used to treat solid tumors. In order to avoid these adverse events, it is necessary to understand the fundamental mechanisms during the physical process of T cell engagement. We developed a multiscale computational framework to reach this goal. The framework combines simulations on the intercellular and multicellular levels. On the intercellular level, we simulated the spatial-temporal dynamics of three-body interactions among bispecific antibodies, CD3 and TAA. The derived number of intercellular bonds formed between CD3 and TAA were further transferred into the multicellular simulations as the input parameter of adhesive density between cells. Through the simulations under various molecular and cellular conditions, we were able to gain new insights of how to adopt the most appropriate strategy to maximize the drug efficacy and avoid the off-target effect. For instance, we discovered that the low antibody binding affinity resulted in the formation of large clusters at the cell-cell interface, which could be important to control the downstream signaling pathways. We also tested different molecular architectures of the bispecific antibody and suggested the existence of an optimal length in regulating the T cell engagement. Overall, the current multiscale simulations serve as a prove-of-concept study to help the future design of new biological therapeutics. SIGNIFICANCE: T-cell engagers are a class of anti-cancer drugs that can directly kill tumor cells by bringing T cells next to them. However, current treatments using T-cell engagers can cause serious side-effects. In order to reduce these effects, it is necessary to understand how T cells and tumor cells interact together through the connection of T-cell engagers. Unfortunately, this process is not well studied due to the limitations in current experimental techniques. We developed computational models on two different scales to simulate the physical process of T cell engagement. Our simulation results provide new insights into the general properties of T cell engagers. The new simulation methods can therefore serve as a useful tool to design novel antibodies for cancer immunotherapy.

Computational analyses of the interactome between TNF and TNFR superfamilies.

Proteins in the tumor necrosis factor (TNF) superfamily (TNFSF) regulate diverse cellular processes by interacting with their receptors in the TNF receptor (TNFR) superfamily (TNFRSF). Ligands and receptors in these two superfamilies form a complicated network of interactions, in which the same ligand can bind to different receptors and the same receptor can be shared by different ligands. In order to study these interactions on a systematic level, a TNFSF-TNFRSF interactome was constructed in this study by searching the database which consists of both experimentally measured and computationally predicted protein-protein interactions (PPIs). The interactome contains a total number of 194 interactions between 18 TNFSF ligands and 29 TNFRSF receptors in human. We modeled the structure for each ligand-receptor interaction in the network. Their binding affinities were further computationally estimated based on modeled structures. Our computational outputs, which are all publicly accessible, serve as a valuable addition to the currently limited experimental resources to study TNF-mediated cell signaling.

How does the same ligand activate signaling of different receptors in TNFR superfamily: a computational study.

TNFα is a highly pleiotropic cytokine inducing inflammatory signaling pathways. It is initially presented on plasma membrane of cells (mTNFα), and also exists in a soluble variant (sTNFα) after cleavage. The ligand is shared by two structurally similar receptors, TNFR1 and TNFR2. Interestingly, while sTNFα preferentially stimulates TNFR1, TNFR2 signaling can only be activated by mTNFα. How can two similar receptors respond to the same ligand in such a different way? We employed computational simulations in multiple scales to address this question. We found that both mTNFα and sTNFα can trigger the clustering of TNFR1. The size of clusters induced by sTNFα is constantly larger than the clusters induced by mTNFα. The systems of TNFR2, on the other hand, show very different behaviors. Only when the interactions between TNFR2 are very weak, mTNFα can trigger the receptors to form very large clusters. Given the same weak binding affinity, only small oligomers were obtained in the system of sTNFα. Considering that TNF-mediated signaling is modulated by the ligand-induced clustering of receptors on cell surface, our study provided the mechanistic foundation to the phenomenon that different isoforms of the ligand can lead to highly distinctive signaling patterns for its receptors.

Lactobacillus plantarum 17-5 attenuates Escherichia coli-induced inflammatory responses via inhibiting the activation of the NF-κB and MAPK signalling pathways in bovine mammary epithelial cells.

BACKGROUND: Mastitis is one of the most prevalent diseases and causes considerable economic losses in the dairy farming sector and dairy industry. Presently, antibiotic treatment is still the main method to control this disease, but it also brings bacterial resistance and drug residue problems. Lactobacillus plantarum (L. plantarum) is a multifunctional probiotic that exists widely in nature. Due to its anti-inflammatory potential, L. plantarum has recently been widely researched in complementary therapies for various inflammatory diseases. In this study, the apoptotic ratio, the expression levels of various inflammatory mediators and key signalling pathway proteins in Escherichia coli-induced bovine mammary epithelial cells (BMECs) under different doses of L. plantarum 17-5 intervention were evaluated. RESULTS: The data showed that L. plantarum 17-5 reduced the apoptotic ratio, downregulated the mRNA expression levels of TLR2, TLR4, MyD88, IL1ß, IL6, IL8, TNFα, COX2, iNOS, CXCL2 and CXCL10, and inhibited the activation of the NF-κB and MAPK signalling pathways by suppressing the phosphorylation levels of p65, IκBα, p38, ERK and JNK. CONCLUSIONS: The results proved that L. plantarum 17-5 exerted alleviative effects in Escherichia coli-induced inflammatory responses of BMECs.

Computational Assessment of Protein-protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes.

The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein-protein interactions.

A multiscale study on the mechanisms of spatial organization in ligand-receptor interactions on cell surfaces.

The binding of cell surface receptors with extracellular ligands triggers distinctive signaling pathways, leading into the corresponding phenotypic variation of cells. It has been found that in many systems, these ligand-receptor complexes can further oligomerize into higher-order structures. This ligand-induced oligomerization of receptors on cell surfaces plays an important role in regulating the functions of cell signaling. The underlying mechanism, however, is not well understood. One typical example is proteins that belong to the tumor necrosis factor receptor (TNFR) superfamily. Using a generic multiscale simulation platform that spans from atomic to subcellular levels, we compared the detailed physical process of ligand-receptor oligomerization for two specific members in the TNFR superfamily: the complex formed between ligand TNFα and receptor TNFR1 versus the complex formed between ligand TNFß and receptor TNFR2. Interestingly, although these two systems share high similarity on the tertiary and quaternary structural levels, our results indicate that their oligomers are formed with very different dynamic properties and spatial patterns. We demonstrated that the changes of receptor's conformational fluctuations due to the membrane confinements are closely related to such difference. Consistent to previous experiments, our simulations also showed that TNFR can preassemble into dimers prior to ligand binding, while the introduction of TNF ligands induced higher-order oligomerization due to a multivalent effect. This study, therefore, provides the molecular basis to TNFR oligomerization and reveals new insights to TNFR-mediated signal transduction. Moreover, our multiscale simulation framework serves as a prototype that paves the way to study higher-order assembly of cell surface receptors in many other bio-systems.

Mechanistic dissection of spatial organization in NF-κB signaling pathways by hybrid simulations.

The nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) is one of the most important transcription factors involved in the regulation of inflammatory signaling pathways. Inappropriate activation of these pathways has been linked to autoimmunity and cancers. Emerging experimental evidences have been showing the existence of elaborate spatial organizations for various molecular components in the pathways. One example is the scaffold protein tumor necrosis factor receptor associated factor (TRAF). While most TRAF proteins form trimeric quaternary structure through their coiled-coil regions, the N-terminal region of some members in the family can further be dimerized. This dimerization of TRAF trimers can drive them into higher-order clusters as a response to receptor stimulation, which functions as a spatial platform to mediate the downstream poly-ubiquitination. However, the molecular mechanism underlying the TRAF protein clustering and its functional impacts are not well-understood. In this article, we developed a hybrid simulation method to tackle this problem. The assembly of TRAF-based signaling platform at the membrane-proximal region is modeled with spatial resolution, while the dynamics of downstream signaling network, including the negative feedbacks through various signaling inhibitors, is simulated as stochastic chemical reactions. These two algorithms are further synchronized under a multiscale simulation framework. Using this computational model, we illustrated that the formation of TRAF signaling platform can trigger an oscillatory NF-κB response. We further demonstrated that the temporal patterns of downstream signal oscillations are closely regulated by the spatial factors of TRAF clustering, such as the geometry and energy of dimerization between TRAF trimers. In general, our study sheds light on the basic mechanism of NF-κB signaling pathway and highlights the functional importance of spatial regulation within the pathway. The simulation framework also showcases its potential of application to other signaling pathways in cells.

A Systematic Test of Receptor Binding Kinetics for Ligands in Tumor Necrosis Factor Superfamily by Computational Simulations.

Ligands in the tumor necrosis factor (TNF) superfamily are one major class of cytokines that bind to their corresponding receptors in the tumor necrosis factor receptor (TNFR) superfamily and initiate multiple intracellular signaling pathways during inflammation, tissue homeostasis, and cell differentiation. Mutations in the genes that encode TNF ligands or TNFR receptors result in a large variety of diseases. The development of therapeutic treatment for these diseases can be greatly benefitted from the knowledge on binding properties of these ligand-receptor interactions. In order to complement the limitations in the current experimental methods that measure the binding constants of TNF/TNFR interactions, we developed a new simulation strategy to computationally estimate the association and dissociation between a ligand and its receptor. We systematically tested this strategy to a comprehensive dataset that contained structures of diverse complexes between TNF ligands and their corresponding receptors in the TNFR superfamily. We demonstrated that the binding stabilities inferred from our simulation results were compatible with existing experimental data. We further compared the binding kinetics of different TNF/TNFR systems, and explored their potential functional implication. We suggest that the transient binding between ligands and cell surface receptors leads into a dynamic nature of cross-membrane signal transduction, whereas the slow but strong binding of these ligands to the soluble decoy receptors is naturally designed to fulfill their functions as inhibitors of signal activation. Therefore, our computational approach serves as a useful addition to current experimental techniques for the quantitatively comparison of interactions across different members in the TNF and TNFR superfamily. It also provides a mechanistic understanding to the functions of TNF-associated cell signaling pathways.

A computational model for understanding the oligomerization mechanisms of TNF receptor superfamily.

By recognizing members in the tumor necrosis factor (TNF) receptor superfamily, TNF ligand proteins function as extracellular cytokines to activate various signaling pathways involved in inflammation, proliferation, and apoptosis. Most ligands in TNF superfamily are trimeric and can simultaneously bind to three receptors on cell surfaces. It has been experimentally observed that the formation of these molecular complexes further triggers the oligomerization of TNF receptors, which in turn regulate the intracellular signaling processes by providing transient compartmentalization in the membrane proximal regions of cytoplasm. In order to decode the molecular mechanisms of oligomerization in TNF receptor superfamily, we developed a new computational method that can physically simulate the spatial-temporal process of binding between TNF ligands and their receptors. The simulations show that the TNF receptors can be organized into hexagonal oligomers. The formation of this spatial pattern is highly dependent not only on the molecular properties such as the affinities of trans and cis binding, but also on the cellular factors such as the concentration of TNF ligands in the extracellular area or the density of TNF receptors on cell surfaces. Moreover, our model suggests that if TNF receptors are pre-organized into dimers before ligand binding, these lateral interactions between receptor monomers can play a positive role in stabilizing the ligand-receptor interactions, as well as in regulating the kinetics of receptor oligomerization. Altogether, this method throws lights on the mechanisms of TNF ligand-receptor interactions in cellular environments.

Computational simulations of TNF receptor oligomerization on plasma membrane.

The interactions between tumor necrosis factors (TNFs) and their corresponding receptors (TNFRs) play a pivotal role in inflammatory responses. Upon ligand binding, TNFR receptors were found to form oligomers on cell surfaces. However, the underlying mechanism of oligomerization is not fully understood. In order to tackle this problem, molecular dynamics (MD) simulations have been applied to the complex between TNF receptor-1 (TNFR1) and its ligand TNF-α as a specific test system. The simulations on both all-atom (AA) and coarse-grained (CG) levels achieved the similar results that the extracellular domains of TNFR1 can undergo large fluctuations on plasma membrane, while the dynamics of TNFα-TNFR1 complex is much more constrained. Using the CG model with the Martini force field, we are able to simulate the systems that contain multiple TNFα-TNFR1 complexes with the timescale of microseconds. We found that complexes can aggregate into oligomers on the plasma membrane through the lateral interactions between receptors at the end of the CG simulations. We suggest that this spatial organization is essential to the efficiency of signal transduction for ligands that belong to the TNF superfamily. We further show that the aggregation of two complexes is initiated by the association between the N-terminal domains of TNFR1 receptors. Interestingly, the cis-interfaces between N-terminal regions of two TNF receptors have been observed in the previous X-ray crystallographic experiment. Therefore, we provide supportive evidence that cis-interface is of functional importance in triggering the receptor oligomerization. Taken together, our study brings insights to understand the molecular mechanism of TNF signaling.

A Computational Model for Kinetic Studies of Cadherin Binding and Clustering.

Cadherin is a cell-surface transmembrane receptor that mediates calcium-dependent cell-cell adhesion and is a major component of adhesive junctions. The formation of intercellular adhesive junctions is initiated by trans binding between cadherins on adjacent cells, which is followed by the clustering of cadherins via the formation of cis interactions between cadherins on the same cell membranes. Moreover, classical cadherins have multiple glycosylation sites along their extracellular regions. It was found that aberrant glycosylation affects the adhesive function of cadherins and correlates with metastatic phenotypes of several cancers. However, a mechanistic understanding of cadherin clustering during cell adhesion and the role of glycosylation in this process is still lacking. Here, we designed a kinetic model that includes multistep reaction pathways for cadherin clustering. We further applied a diffusion-reaction algorithm to numerically simulate the clustering process using a recently developed coarse-grained model. Using experimentally measured rates of trans binding between soluble E-cadherin extracellular domains, we conducted simulations of cadherin-mediated cell-cell binding kinetics, and the results are quantitatively comparable to experimental data from micropipette experiments. In addition, we show that incorporating cadherin clustering via cis interactions further increases intercellular binding. Interestingly, a two-phase kinetic profile was derived under the assumption that glycosylation regulates the kinetic rates of cis interactions. This two-phase profile is qualitatively consistent with experimental results from micropipette measurements. Therefore, our computational studies provide new, to our knowledge, insights into the molecular mechanism of cadherin-based cell adhesion.

Development of a Dinitrosyl Iron Complex Molecular Catalyst into a Hydrogen Evolution Cathode.

Despite extensive efforts, the electrocatalytic reduction of water using homogeneous/heterogeneous Fe, Co, Ni, Cu, W, and Mo complexes remains challenging because of issues involving the development of efficient, recyclable, stable, and aqueous-compatible catalysts. In this study, evolution of the de novo designed dinitrosyl iron complex DNIC-PMDTA from a molecular catalyst into a solid-state hydrogen evolution cathode, considering all the parameters to fulfill the electronic and structural requirements of each step of the catalytic cycle, is demonstrated. DNIC-PMDTA reveals electrocatalytic reduction of water at neutral and basic media, whereas its deposit on electrode preserves exceptional longevity, 139 h. This discovery will initiate a systematic study on the assembly of [Fe(NO)2] motif into current collector for mass production of H2, whereas the efficiency remains tailored by its molecular precursor [(L)Fe(NO)2].

Computational modeling of the interplay between cadherin-mediated cell adhesion and Wnt signaling pathway.

Wnt signaling and cadherin-mediated adhesion have been implicated in both processes of embryonic development and the progression of carcinomas. Recent experimental studies revealed that Wnt signaling and cadherin-mediated cell adhesion have close crosstalk with each other. A comprehensive model that investigates the dynamic balance of ß-catenins in Wnt signaling and cell adhesion will improve our understanding to embryonic development and carcinomas. We constructed a network model to evaluate the dynamic interplay between adhesion and Wnt signaling. The network is decomposed into three interdependent modules: the cell adhesion, the degradation circle and the transcriptional regulation. In the cell adhesion module, we consider the effect of cadherin's lateral clustering. We found adhesion negatively contributes to Wnt signaling through competition for cytoplasmic ß-catenins. In the network of degradation circle, we incorporated features from various existing models. Our simulations reproduced the most recent experimental phenomena with semi-quantitative accuracy. Finally, in the transcriptional regulation module, we developed a function selection strategy to analyze the outcomes of genetic feedback loops in modulating the gene expression of Wnt targets. The specific cellular phenomena such as cadherin switch and Axin oscillation were archived and their biological insights were discussed. Our model provides the theoretical basis of how spatial organization regulates the dynamics of cellular signaling pathways. We suggest that cell adhesion affects Wnt signaling in both negative and positive ways. Cadherins can inhibit Wnt signaling not only in a way as a stoichiometric binding partner of ß-catenins that sequesters them from signaling, but also in a way through their clustering to impacts the rate at which ß-catenins are involved in the destruction loop. Additionally, cadherin clustering increases the phosphorylation rate of ß-catenins and promotes its signaling in nucleus.

OPUS-Dom: applying the folding-based method VECFOLD to determine protein domain boundaries.

In this article, we present a de novo method for predicting protein domain boundaries, called OPUS-Dom. The core of the method is a novel coarse-grained folding method, VECFOLD, which constructs low-resolution structural models from a target sequence by folding a chain of vectors representing the predicted secondary-structure elements. OPUS-Dom generates a large ensemble of folded structure decoys by VECFOLD and labels the domain boundaries of each decoy by a domain parsing algorithm. Consensus domain boundaries are then derived from the statistical distribution of the putative boundaries and three empirical sequence-based domain profiles. OPUS-Dom generally outperformed several state-of-the-art domain prediction algorithms over various benchmark protein sets. Even though each VECFOLD-generated structure contains large errors, collectively these structures provide a more robust delineation of domain boundaries. The success of OPUS-Dom suggests that the arrangement of protein domains is more a consequence of limited coordination patterns per domain arising from tertiary packing of secondary-structure segments, rather than sequence-specific constraints.

Substructure synthesis method for simulating large molecular complexes.

This paper reports a computational method for describing the conformational flexibility of very large biomolecular complexes using a reduced number of degrees of freedom. It is called the substructure synthesis method, and the basic concept is to treat the motions of a given structure as a collection of those of an assemblage of substructures. The choice of substructures is arbitrary and sometimes quite natural, such as domains, subunits, or even large segments of biomolecular complexes. To start, a group of low-frequency substructure modes is determined, for instance by normal mode analysis, to represent the motions of the substructure. Next, a desired number of substructures are joined together by a set of constraints to enforce geometric compatibility at the interface of adjacent substructures, and the modes for the assembled structure can then be synthesized from the substructure modes by applying the Rayleigh-Ritz principle. Such a procedure is computationally much more desirable than solving the full eigenvalue problem for the whole assembled structure. Furthermore, to show the applicability to biomolecular complexes, the method is used to study F-actin, a large filamentous molecular complex involved in many cellular functions. The results demonstrate that the method is capable of studying the motions of very large molecular complexes that are otherwise completely beyond the reach of any conventional methods.