AREA-AFFINITY: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities.

Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.

Factors and Mechanisms Affecting the Secretion of Recombinant Protein in CHO Cells.

The market demand for recombinant therapeutic proteins (RTPs) has promoted the development of various protein expression host and bioprocessing technologies. Since mammalian cells have the unique advantage of being able to direct the correct folding of proteins and provide post-translational processing such as complex glycosylation, the RTPs produced by them currently account for approximately 80% of the approved marketed RTPs. Among them, Chinese hamster ovary (CHO) cells are currently the preferred host cells for the production of RTPs. Production of RTPs in CHO cells involves the synthesis, processing, transport, and secretion of proteins. The secretion process of proteins is one of the key steps, which greatly limits the yield and quality of RTPs. Here, we review the recombinant protein secretion process of CHO cells and its influencing factors, and further discuss the optimization strategy for recombinant protein secretion and expression in CHO cells.

Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas.

Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.

Stabilizing and Anti-Repressor Elements Effectively Increases Transgene Expression in Transfected CHO Cells.

Chinese hamster ovary (CHO) cells are currently the most widely used host cells for recombinant therapeutic protein (RTP) production. Currently, the RTP yields need to increase further to meet the market needs and reduce costs. In this study, three stabilizing and anti-repressor (SAR) elements from the human genome were selected, including human SAR7, SAR40, and SAR44 elements. SAR elements were cloned upstream of the promoter in the eukaryotic vector, followed by transfection into CHO cells, and were screened under G418 pressure. Flow cytometry was used to detect enhanced green fluorescent protein (eGFP) expression levels. The gene copy numbers and mRNA expression levels were determined through quantitative real-time PCR. Furthermore, the effect of the stronger SAR elements on adalimumab was investigated. The results showed that transgene expression levels in the SAR-containing vectors were higher than that of the control vector, and SAR7 and SAR40 significantly increased and maintained the long-term expression of the transgene in CHO cells. In addition, the transgene expression level increase was related with gene copy numbers and mRNA expression levels. Collectively, SAR elements can enhance the transgene expression and maintain the long-term expression of a transgene in transfected CHO cells, which may be used to increase recombinant protein production in CHO cells.

Importance of interface and surface areas in protein-protein binding affinity prediction: A machine learning analysis based on linear regression and artificial neural network.

Protein-protein interaction plays an important role in all biological systems. The binding affinity between two protein binding partners reflects the strength of their association, which is crucial to the elucidation of the biological functions of these proteins and also to the design of protein-based therapeutic agents. In recent years, many studies have been conducted in an effort to improve the ability to predict the binding affinity of a protein-protein complex. Different sequence and structural features have been adopted in the prediction, but the surface or interface areas of the protein-protein complex were often not given adequate consideration. In the present study, different types of interface and surface areas in the protein-protein complex were used to construct or train linear, nonlinear or mixed models using linear regression and artificial neural network to predict the binding affinity of protein-protein interactions. The relative importance of the different types of areas in the selected models for affinity prediction was analyzed using variable-controlling approach. In terms of performance, the best area-based binding affinity predictors appeared to be superior or at least comparable to the widely-used predictors PRODIGY (a contacts-based predictor) and LISA (Local Interaction Signal Analysis). This work highlights the importance of interface and surface areas in protein-protein binding interactions. It also sheds light on the more suitable computational approaches that may aid in solving some of the scientific and technical issues associated with protein-protein binding affinity prediction. SIGNIFICANCE: Protein-protein interactions are ubiquitous in living systems. Protein-protein binding affinity is a metric that estimates the binding strength between two protein binding partners. Reliable information on their binding affinity is of great value in understanding complex biological processes as well as in designing protein-based therapeutics. In this work, the interface and surface areas in protein-protein interaction are explored with respect to their relative importance in better predicting the protein-protein binding affinity. The results from this study showed that different types of areas contribute importantly to protein-protein interactions and thus should be jointly considered in an explicit manner to improve affinity predictions. In addition, the effective application of interface and surface areas may also facilitate the simulation of the protein folding and binding processes.

17ß-Estradiol-Induced Conformational Changes of Human Microsomal Triglyceride Transfer Protein: A Computational Molecular Modelling Study.

Human microsomal triglyceride transfer protein (hMTP) plays an essential role in the assembly of apoB-containing lipoproteins, and has become an important drug target for the treatment of several disease states, such as abetalipoproteinemia, fat malabsorption and familial hypercholesterolemia. hMTP is a heterodimer composed of a larger hMTPα subunit and a smaller hMTPß subunit (namely, protein disulfide isomerase, hPDI). hPDI can interact with 17ß-estradiol (E2), an endogenous female sex hormone. It has been reported that E2 can significantly reduce the blood levels of low-density lipoprotein, cholesterol and triglyceride, and modulate liver lipid metabolism in vivo. However, some of the estrogen's actions on lipid metabolism are not associated with estrogen receptors (ER), and the exact mechanism underlying estrogen's ER-independent lipid-modulating action is still not clear at present. In this study, the potential influence of E2 on the stability of the hMTP complex is investigated by jointly using multiple molecular dynamics analyses based on available experimental structures. The molecular dynamics analyses indicate that the hMTP complex in the presence of E2 has reduced interface contacts and surface areas. A steered molecular dynamics analysis shows that the forces required to separate the two subunits (namely, hPDI and hMTPα subunit) of the hMTP complex in the absence of E2 are significantly higher than the forces required to separate the complex in which its hPDI is already bound with E2. E2 makes the interface between hMTPα and hPDI subunits more flexible and less stable. The results of this study suggest that E2-induced conformational changes of the hMTP complex might be a novel mechanism partly accounting for the ER-independent lipid-modulating effect of E2.

Insulin-induced conformational changes in the full-length insulin receptor: structural insights gained from molecular modeling analyses.

Insulin receptor plays an important role in the regulation of energy metabolism. Dysfunction of insulin receptor (IR) can lead to many disease states, such as diabetes mellitus. Deciphering the complex dynamic structures of human IR and its mechanism of activation would greatly aid in understanding IR-mediated signaling pathways and also in designing new drugs (including nonpeptidal insulin analogs) to treat diabetes mellitus. Experimental evidence about IR structures has been gradually obtained by biologists over the past three decades. Based on available experimental structures of IR in different states, here we employ molecular modeling approach to construct the full-length IR structures in different states and model its structural and conformational changes during insulin-induced IR activation. Several key possible intermediate states are constructed based on structural alignment, rotation, and computational modeling. Based on the structures of the full-length IR in different states, it appears that there are two possible conformational transition pathways: one is symmetric and the other one is asymmetric. Structural changes and motions of different domains of the full-length IR along the pathways are analyzed. The role of insulin binding to IR in facilitating the conformational transition of the receptor is analyzed. Information and insights derived from our present structural modeling analyses may aid in understanding the complex dynamic, structural, and conformational changes during the process of IR activation.

Allosteric transitions of ATP-binding cassette transporter MsbA studied by the adaptive anisotropic network model.

The transporter MsbA is a kind of multidrug resistance ATP-binding cassette transporter that can transport lipid A, lipopolysaccharides, and some amphipathic drugs from the cytoplasmic to the periplasmic side of the inner membrane. In this work, we explored the allosteric pathway of MsbA from the inward- to outward-facing states during the substrate transport process with the adaptive anisotropic network model. The results suggest that the allosteric transitions proceed in a coupled way. The large-scale closing motions of the nucleotide-binding domains occur first, accompanied with a twisting motion at the same time, which becomes more obvious in middle and later stages, especially for the later. This twisting motion plays an important role for the rearrangement of transmembrane helices and the opening of transmembrane domains on the periplasmic side that mainly take place in middle and later stages respectively. The topological structure plays an important role in the motion correlations above. The conformational changes of nucleotide-binding domains are propagated to the transmembrane domains via the intracellular helices IH1 and IH2. Additionally, the movement of the transmembrane domains proceeds in a nonrigid body, and the two monomers move in a symmetrical way, which is consistent with the symmetrical structure of MsbA. These results are helpful for understanding the transport mechanism of the ATP-binding cassette exporters.

Allosteric transitions of the maltose transporter studied by an elastic network model.

The maltose transporter from Escherichia coli is one of the ATP-binding cassette (ABC) transporters that utilize the energy from ATP hydrolysis to translocate substrates across cellular membranes. Until 2011, three crystal structures have been determined for maltose transporter at different states in the process of transportation. Here, based on these crystal structures, the allosteric pathway from the resting state (inward-facing) to the catalytic intermediate state (outward-facing) is studied by applying an adaptive anisotropic network model. The results suggest that the allosteric transitions proceed in a coupled way. The closing of the nucleotide-binding domains occurs first, and subsequently this conformational change is propagated to the transmembrane domains (TMD) via the EAA and EAS loops, and then to the maltose-binding protein, which facilitates the translocation of the maltose. It is also found that there exist nonrigid-body and asymmetric movements in the TMD. The cytoplasmic gate may only play the role of allosteric propagation during the transition from the pretranslocation to outward-facing states. In addition, the results show that the movment of the helical subdomain towards the RecA-like subdomain mainly occurs in the earlier stages of the transition. These results can provide some insights into the understanding of the mechanism of ABC transporters.

A new residue-nucleotide propensity potential with structural information considered for discriminating protein-RNA docking decoys.

Understanding the key factors that influence the preferences of residue-nucleotide interactions in specific protein-RNA interactions has remained a research focus. We propose an effective approach to derive residue-nucleotide propensity potentials through considering both the types of residues and nucleotides, and secondary structure information of proteins and RNAs from the currently largest nonredundant and nonribosomal protein-RNA interaction database. To test the validity of the potentials, we used them to select near-native structures from protein-RNA docking poses. The results show that considering secondary structure information, especially for RNAs, greatly improves the predictive power of pair potentials. The success rate is raised from 50.7 to 65.5% for the top 2000 structures, and the number of cases in which a near-native structure is ranked in top 50 is increased from 7 to 13 out of 17 cases. Furthermore, the exclusion of ribosomes from the database contributes 8.3% to the success rate. In addition, some very interesting findings follow: (i) the protein secondary structure element π-helix is strongly associated with RNA-binding sites; (ii) the nucleotide uracil occurs frequently in the most preferred pairs in which the unpaired and non-Watson-Crick paired uracils are predominant, which is probably significant in evolution. The new residue-nucleotide potentials can be helpful for the progress of protein-RNA docking methods, and for understanding the mechanisms of protein-RNA interactions.