R-3 Repository :: Browsing by Author "Ma, Jianpeng"

Browsing by Author "Ma, Jianpeng"

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A Novel Statistical Potential for Protein Beta-Sheets Prediction
(2014-04-25) Yu, Linglin; Ma, Jianpeng; Nordlander, Peter J.; Raphael, Robert M.
One of the most long-term challenging problems in biophysics studies for both computational scientists and experimentalists is protein structure prediction, whose goal is to obtain three-dimensional native protein structure from one-dimensional sequence. In protein structure prediction problems, a fundamental problem is Beta-sheets structure prediction. Though more than 85% of experimentally solved proteins contain Beta-sheet structures, limited methods have been found to rapidly and accurately predict the folded conformations. In this study, we proposed a novel statistical potential, named NP-Beta, to predict the protein Beta-sheet structure only based on the sequence information. We included three kinds of potential terms in NP-Beta, i.e. the self-packing term, the pair interacting term and the lattice term. The number of hydrogen bonds in Beta-sheets is also considered as a potential component, corresponding to a global penalty of the potential function. Computational tests show that the new statistical potential has an outstanding performance on native structure recognition from decoys comparing to the Beta-sheet specific potentials in literature. We will apply the potential to improve the prediction of Beta-strand arrangement and registration for beta proteins.
A series of advanced scoring functions in ranking protein structures
(2021-05-13) Ma, Tianqi; Ma, Jianpeng
Designing an efficient scoring function is one of the most challenging tasks in computational biology. A good potential functions or scoring functions can help rank protein structures models, guide the search and identify possible solutions. This is very important for protein structure prediction. A lot of work have been done in this area but none of them has achieved the desired result. It is therefore urgently needed to develop good scoring functions to accelerate the process. In this thesis, I will present several novel empirical potential functions and scoring functions to address this problem. First, an upgraded version of previous work OPUS-PSP, named OPUS-DOSP. A distance related term is added to the potential function and the performance is improved. Second, a non-traditional scoring function, named OPUS-CSF is developed. This scoring function didn’t use the traditional Boltzmann formula but constructed a native configuration distribution table instead. This scoring function outperformed the previous work, OPUS-DOSP. Thirdly, two scoring functions combining the features of previous two scoring functions are developed. OPUS-SSF and OPUS-Beta are their names. These two scoring functions yield the best result so far and are promising in this area. The effectiveness of these scoring functions is tested in various decoy sets generated from native structures. In the traditional benchmarks like ROSETTA, ig_structure, fisa_casp3, MOULDER and so on, OPUS-DOSP, OPUS-CSF, OPUS-SSF are performing better than previous works. In beta-prediction benchmarks Beta916 and Beta1452, OPUS-Beta also outperformed existing methods. Therefore, these scoring functions seem to be promising and useful in this field.
Ab initio methods for protein structure prediction
(2010) Dousis, Athanasios Dimitri; Ma, Jianpeng
Recent breakthroughs in DNA and protein sequencing have unlocked many secrets of molecular biology. A complete understanding of gene function, however, requires a protein structure in addition to its sequence. Modern protein structure determination methods such as NMR, cryo-EM and X-ray crystallography are woefully unable to keep pace with automated sequencing techniques, creating a serious gap between available sequences and structures. This thesis describes several ab initio computational methods designed in the near-term to facilitate structure determination experiments, and in the long-term goal to predict protein structure completely and reliably. First, VecFold is a novel method for predicting the global tertiary structure topologies of proteins. VecFold applies fragment assembly to construct structural models from a target sequence by folding a chain of predicted secondary structure elements; these elements are represented either as Calpha-based rigid bodies or as vectors. The knowledge-based energy function OPUS-Ca or a knowledge-based geometric packing potential is used to guide the folding process. The newest version of VecFold is demonstrated to modestly outperform Rosetta, one of the leading ab initio predictors, on the CASP8 benchmark set. In our protein domain boundary prediction method OPUS-Dom, VecFold generates a large ensemble of folded structure models, and the domain boundaries of each model are labeled by a domain parsing algorithm. OPUS-Dom then derives consensus domain boundaries from the statistical distribution of the putative boundaries; the original version is also aided by three empirical sequence-based domain profiles. The latest version of OPUS-Dom outperformed, in terms of prediction sensitivity, several state-of-the-art domain prediction algorithms over various multi-domain protein sets. Even though many VecFold-generated structures contain 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. Finally, the knowledge-based energy function OPUS-Core was applied to the problem of protein folding core prediction, and it was shown to outpredict two leading computational methods on a benchmark set of 29 well-characterized protein targets.
Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models
(2016-11-10) Zang, Tianwu; Ma, Jianpeng
Predicting the 3-dimentional structure of protein has been a major interest in the modern computational biology. While lots of successful methods can generate models with 3~5Å root-mean-square deviation (RMSD) from the solution, the progress of refining these models is quite slow. It is therefore urgently needed to develop effective methods to bring low-quality models to higher-accuracy ranges (e.g., less than 2 Å RMSD). In this thesis, I present several novel computational methods to address the high-accuracy refinement problem. First, an enhanced sampling method, named parallel continuous simulated tempering (PCST), is developed to accelerate the molecular dynamics (MD) simulation. Second, two energy biasing methods, Structure-Based Model (SBM) and Ensemble-Based Model (EBM), are introduced to perform targeted sampling around important conformations. Third, a three-step method is developed to blindly select high-quality models along the MD simulation. These methods work together to make significant refinement of low-quality models without any knowledge of the solution. The effectiveness of these methods is examined in different applications. Using the PCST-SBM method, models with higher global distance test scores (GDT_TS) are generated and selected in the MD simulation of 18 targets from the refinement category of the 10th Critical Assessment of Structure Prediction (CASP10). In addition, in the refinement test of two CASP10 targets using the PCST-EBM method, it is indicated that EBM may bring the initial model to even higher-quality levels. Furthermore, a multi-round refinement protocol of PCST-SBM improves the model quality of a protein to the level that is sufficient high for the molecular replacement in X-ray crystallography. Our results justify the crucial position of enhanced sampling in the protein structure prediction and demonstrate that a considerable improvement of low-accuracy structures is still achievable with current force fields.
Advanced Computational Methods for Macromolecular Modeling and Structure Determination
(2013-12-05) Zhang, Chong; Ma, Jianpeng; Nordlander, Peter J.; Kiang, Ching-Hwa; Raphael, Robert M.
As volume and complexity of macromolecules increase, theories and algorithms that deal with structure determination at low X-ray resolution are of particular importance. With limited diffraction data in hand, experimentalists rely on advanced computational tools to extract and utilize useful information, seeking to determinate a three dimensional model that best fits the experiment data. Success of further studies on the property and function of a specific molecule - the key to practical applications - is therefore heavily dependent on the validity and accuracy of the solved structure. In this thesis I propose Deformable Complex Network (DCN) and introduce Normal Mode Analysis (NMA), which are designed to model the average coordinates of atoms and associated fluctuations, respectively. Their applications on structure determination target two major branches ? the positional refinement and temperature factor refinement. I demonstrate their remarkable performance in structure improvements based on several criteria, such as the free R value, overfitting effect and Ramachandran Statistics, with tests carried out across a broad range of real systems for generality and consistency.
Coarse-grained Direct Phasing Method for Protein X-ray Crystallography
(2013-11-04) Chen, Dong; Ma, Jianpeng; Nordlander, Peter J.; Raphael, Robert M.
X-ray crystallography is the most powerful method to obtain the structural of biological molecules if the “phase problem” can be solved for the molecules under study. The phase problem arises from the loss of phase information in diffraction experiment. In all the solutions of the phase problem, the direct method is the only one that does not require additional experimental data or knowledge of homologous structures. It can determine the phase information directly from the observed structure factor magnitudes or intensities. However, the direct phasing method has limitations when applying to macromolecule. It is only applicable in molecules with up to about 1000 non-H atoms and requires ultra-high resolution (the Sheldrick's 1.2 Å rule) diffraction data that is not available in most protein crystallography experiments. To overcome the two limitations, here we propose a coarse-grained direct phasing method. This thesis will focus on how to break the 1.2 Å resolution requirement.
Comparative analysis of Influenza virus evolution
(2020-12-01) Lee, Sungmin; Ma, Jianpeng
Occurrences of newly emerging or re-emerging influenza viral infection present significant challenges to global public health. The causative virus, influenza type A and type B virus, is responsible for annual global epidemics and periodic pandemics. Continuing antigenic drift and genetic shift allow the emergence of new human strains of influenza virus. Therefore, it is critically important to understand the pattern of virus circulation and evolutionary in order to develop a plan for influenza control and prevention. In this dissertation, I analyzed patterns of viral circulation and evolutionary dy- namics for influenza A/H7N9 and influenza B using phylogenetic analysis and com- putational simulation. For influenza A/H7N9 virus, I performed Bayesian phylogeographic analysis to study the patterns of viral dissemination of low and highly pathogenic viruses isolated in China, and analyzed selection pressure, amino acid variations, and patterns of reassortment. This result revealed that although the two viruses evolve at similar rates, each possesses distinct evolutionary trend. Furthermore, I identified unparallel diffusion dynamics and mismatched spatial transmission predictor between these two viruses. For influenza B virus, I conducted phylodynamic analysis on two strains in the influenza B virus Victoria clade that are currently circulating, namely clade 1A.1 and clade 1A.3. I determined the transmission network unique to each virus and assessed amino acid variations that led to predominant circulation of clade 1A.3 virus in US during 2019-2020 season. I also identified possible co-assorting segments which may further stabilize viral fitness of clade 1A.3 virus. These novel findings presented likely aid in future control and prevention of in- fluenza A/H7N9 and Influenza B viruses.
Computational Biology: Insights into Hemagglutinin and Polycomb Repressive Complex 2 Function
(2012) Kirk, Brian David; Ma, Jianpeng
Influenza B virus hemagglutinin (HA) is a major surface glycoprotein with frequent amino-acid substitutions. However, the roles of antibody selection in the amino-acid substitutions of HA were still poorly understood. An analysis was conducted on a total of 271 HA 1 sequences of influenza B virus strains isolated during 1940âˆ¼2007 finding positively selected sites all located in the four major epitopes (120-loop, 150-loop, 160-loop and 190-helix) supporting a predominant role of antibody selection in HA evolution. Of particular significance is the involvement of the 120-loop in positive selection. Influenza B virus HA continues to evolve into new sublineages, within which the four major epitopes were targeted selectively in positive selection. Thus, any newly emerging strains need to be placed in the context of their evolutionary history in order to understand and predict their epidemic potential. As key epigenetic regulators, polycomb group (PcG) proteins are responsible for the control of cell proliferation and differentiation as well as stem cell pluripotency and self-renewal. To facilitate experimental identification of PcG target genes, which are poorly understood, we propose a novel computational method, EpiPredictor , which models transcription factor interaction using a non-linear kernel. The resulting targets suggests that multiple transcription factor networking at the cis -regulatory elements is critical for PcG recruitment, while high GC content and high conservation level are also important features of PcG target genes. To try to translate the EpiPredictor into human data, we performed a computational study utilizing 22 human genome-wide CHIP data to identify DNA motifs and genome features that would potentially specify PRC2 using five motif discovery algorithms, Jaspar known transcription binding motifs, and other whole genome data. We have found multiple motifs within the various subgroups of experimental categories that have much higher enrichment against CHIP identified gene promoter than among random gene promoters. Specifically, we have identified Low CpG content CpG Islands (LeG's) as being critical in the separation of Cancer cell line identified targets from Embryonic Stem cell line identified targets. Additionally, there are differences between human and mouse ES cell predictions using the same motifs and features suggesting relevant evolutionary divergence.
Computational simulations of supermolecular complexes
(2004) Flynn, Terence C.; Ma, Jianpeng
Computational simulation techniques, targeted molecular dynamics (TMD) and the quantized elastic deformational model (QEDM, a modified normal mode analysis) in particular, have been employed to determine functionally relevant motions (motions required to perform a specific biological process) of six supermolecular complexes (SMC's): F1F0-ATP synthase, lactose repressor protein, HIV-1 reverse transcriptase, AAA p97, lipid bilayer, and bacterial flagellum. These SMC's are involved in a diverse number of biological processes - production of energy, genetic and allosteric regulation, propagation of viral infection, and cell structure integrity. Understanding the motions of SMC's, as opposed to individual proteins or molecules, is a fundamental step towards determining detailed functional mechanisms. (1) TMD trajectories of F1-ATPase are used to resolve the motions and interactions that occur during the 120° rotation step of the gamma subunit. An ionic track of arginine and lysine residues on the protruding portion of the gamma subunit plays a role in guiding the motions of the beta subunits. (2) The allosteric transition of lactose repressor protein from the repressed (DNA-bound) to the induced (IPTG-bound) state is simulated using TMD. Non-covalent interactions of three interconnected pathways are described. Pathway 2 involves reorganization at the dimer interface and formation of an H74-H74' pi-stacking intermediate. (3) TMD is utilized to investigate the translocation mechanism of HIV-1 reverse transcriptase. The atomic-level interactions between electrostatic (i.e., K263) and hydrophobic (i.e., W266) residues and the DNA primer strand are highlighted. (4) The proposed negative-cooperative ratchet-mechanism between the D1 and D2 rings of p97 is illustrated by means of a QEDM analysis. (5) The intrinsic fluctuations of a DPPC lipid bilayer are investigated via QEDM and elucidate a low-frequency sound mode. (6) QEDM is used to calculate the dimensionless twist-to-bend ratio (EI/GJ) of bacterial flagellar hook and filament. Both ratios are less than one, indicating that within each structure bending is favored over twisting. A theoretical Young's modulus for the hook is proposed, which is orders of magnitude smaller than experimentally determined Young's moduli of the filament. The research results in this thesis are also placed in context of existing experimental data, and in some cases propose future experimental work.
Crystallographic refinement of thermal parameters using normal modes
(2008) Poon, Billy K.; Ma, Jianpeng
We have developed a software package that models the anisotropic temperature factors of protein structures derived from crystallographic data using normal mode vectors. We hypothesize that the intrinsic flexibility of a structure is largely responsible for the variability in atomic coordinates and can be modeled using normal modes. The package is written in C/C++ and utilizes the CLIPPER libraries from the Collaborative Computational Project Number 4 (CCP4) for handling most crystallographic functions. Furthermore, the file formats and input conventions from CCP4 are used so that our program can be easily integrated into a crystallographer's toolset. The software package was tested on two systems, formiminotransferase cyclodeaminase (FTCD) and the KcsA potassium ion channel. In both cases, the improved description of the thermal fluctuations by the anisotropic models allowed drops in the R values as well as provided better electron density maps for making adjustments to the atomic coordinates and inserting missing atoms. These two examples show that the new normal-mode-based refinement is an effective way for describing anisotropic thermal motions in X-ray structures and is particularly attractive for the refinement of very large and flexible supramolecular complexes at moderate resolutions.
Deformable complex network for refining low-resolution X-ray structures
(International Union of Crystallography, 2015) Zhang, Chong; Wang, Qinghua; Ma, Jianpeng; Applied Physics Program
In macromolecular X-ray crystallography, building more accurate atomic models based on lower resolution experimental diffraction data remains a great challenge. Previous studies have used a deformable elastic network (DEN) model to aid in low-resolution structural refinement. In this study, the development of a new refinement algorithm called the deformable complex network (DCN) is reported that combines a novel angular network-based restraint with the DEN model in the target function. Testing of DCN on a wide range of low-resolution structures demonstrated that it constantly leads to significantly improved structural models as judged by multiple refinement criteria, thus representing a new effective refinement tool for low-resolution structural determination.
Diversifying selective pressure on influenza B virus hemagglutinin
(2009) Shen, Jun; Ma, Jianpeng
Influenza B virus hemagglutinin (HA) is a major surface glycoprotein with frequent amino-acid substitutions. However, the roles of antibody selection in the amino-acid substitutions of HA were still poorly understood. In order to gain insights into this important issue, an analysis was conducted on a total of 271 HA1 sequences of influenza B virus strains isolated during 1940∼2007. In this analysis, PAML (Phylogenetic Analysis by Maximum Likelihood) package was used to detect the existence of positive selection and to identify positively selected sites on HA1. Strikingly, all the positively selected sites were located in the four major epitopes (120-loop, 150-loop, 160-loop and 190-helix) of HA identified in previous studies, thus supporting a predominant role of antibody selection in HA evolution. Of particular significance is the involvement of the 120-loop in positive selection, which may become increasingly important in future field isolates. Despite the absence of different subtypes, influenza B virus HA continued to evolve into new sublineages, within which the four major epitopes were targeted selectively in positive selection. Thus, any newly emerging strains need to be placed in the context of their evolutionary history in order to understand and predict their epidemic potential.
Enhanced sampling and applications in protein folding
(2013-07-24) Zhang, Cheng; Ma, Jianpeng; McNew, James A.; Igoshin, Oleg A.
We show that a single-copy tempering method is useful in protein-folding simulations of large scale and high accuracy (explicit solvent, atomic representation, and physics-based potential). The method uses a runtime estimate of the average potential energy from an integral identity to guide a random walk in the continuous temperature space. It was used for folding three mini-proteins, trpzip2 (PDB ID: 1LE1), trp-cage (1L2Y), and villin headpiece (1VII) within atomic accuracy. Further, using a modification of the method with a dihedral bias potential added on the roof temperature, we were able to fold four larger helical proteins: α3D (2A3D), α3W (1LQ7), Fap1-NRα (2KUB) and S-836 (2JUA). We also discuss how to optimally use simulation data through an integral identity. With the help of a general mean force formula, the identity makes better use of data collected in a molecular dynamics simulation and is more accurate and precise than the common histogram approach.
Enhanced Sampling Method in Statistical Physics and Large-Scale Molecular Simulation of Complex Systems
(2014-04-25) Zang, Tianwu; Ma, Jianpeng; Kiang, Ching-Hwa; Raphael, Robert M.
In large-scale complex systems, traditional computational methods in equilibrium statistical mechanics such as Monte Carlo simulation and molecular dynamics in canonical ensemble often face the broken ergodicity issue, which highly reduces the performance and accuracy of simulation. The past decades have witnessed the development of generalized ensemble, which has significantly enhanced the efficiency of molecular simulation. In this thesis, we get a review of typical generalized ensembles, such as multi-canonical ensemble, parallel tempering, simulating tempering and continuous simulated tempering (CST). We also present a method called parallel continuous simulated tempering(PCST) for enhanced sampling in studying large complex. It mainly inherits and CST method in previous work, while adopts the spirit of parallel tempering, by employing multiple copies with different temperature distributions. The sampling efficiency of PCST was tested in two-dimensional Ising model, Lennard-Jones liquid and all-atom folding simulation of a small globular protein trp-cage in explicit solvent. The results demonstrate that the PCST method has significantly improved sampling efficiency compared with other methods and it is particularly effective in simulating systems with long relaxation time or correlation time.
Estimating statistical distributions using an integral identity
(American Institute of Physics, 2012) Zhang, Cheng; Ma, Jianpeng; Applied Physics Program
We present an identity for an unbiased estimate of a general statistical distribution. The identity computes the distribution density from dividing a histogram sum over a local window by a correction factor from a mean-force integral, and the mean force can be evaluated as a configuration average. We show that the optimal window size is roughly the inverse of the local mean-force fluctuation. The new identity offers a more robust and precise estimate than a previous one by Adib and Jarzynski [J. Chem. Phys. 122, 014114 (2005)]10.1063/1.1829631. It also allows a straightforward generalization to an arbitrary ensemble and a joint distribution of multiple variables. Particularly we derive a mean-force enhanced version of the weighted histogram analysis method. The method can be used to improve distributions computed from molecular simulations. We illustrate the use in computing a potential energy distribution, a volume distribution in a constant-pressure ensemble, a radial distribution function, and a joint distribution of amino acid backbone dihedral angles.
Free energy landscape for the binding process of Huperzine A to acetylcholinesterase
(National Academy of Sciences, 2012) Bai, Fang; Xu, Yechun; Chen, Jing; Liu, Qiufeng; Gu, Junfeng; Wang, Xicheng; Ma, Jianpeng; Li, Honglin; Onuchic, José N.; Jiang, Hualiang; Center for Theoretical Biological Physics
Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering betteror best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (ΔG≠ off). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated.We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/ mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect thismethodology to be widely applicable to drug discovery and development.
Functional and structural studies of influenza B virus hemagglutinin
(2013-09-16) Ni, Fengyun; Ma, Jianpeng; Tao, Yizhi Jane; Matthews, Kathleen S.
Influenza A and B viruses are major causes of seasonal flu epidemics each year. Hemagglutinin (HA) mediates the binding of virus to host cell and the fusion with host membrane. The crystal of HA in complex with antibody that reveals the mechanism by which antibody recognizes HA may not diffract to high resolution, thereby preventing the accurate interpretation of the structural model. The application of normal mode refinement that aims for improving the structure quality at the low resolution is tested. These studies provide some guidelines for future refinement of HA-antibody complex structures. By comparing the residues constituting the base of the receptor binding site of influenza A and B virus HAs, it is found that they share some similarities, except for a Phe at position 95 of influenza B virus hemagglutinin (BHA) versus Tyr in of influenza A virus hemagglutinin (AHA). The recombinant protein BHA containing the F95Y mutation exhibits the increased receptor binding affinity and specificity. However, recombinant viruses with the Phe95Tyr mutation show lower erythrocyte agglutination titer and decreased binding abilities with different cell lines. The replication of the Phe95Tyr mutant virus in mice is also attenuated. These data suggest that the increased receptor binding ability of HA alone is not advantageous to the pathogenesis of the viruses. The structure of BHA2 (a portion of BHA near the C-terminus) at the post-fusion state has been determined to 2.45 Å resolution. This protein forms a hairpin-like conformation rich in -helices. About 70 residues from the N-terminus is a three-stranded coiled coil, and the remaining of the protein packs in anti-parallel against the groove formed by the central helices. In the post-fusion state of BHA2, the helix converted from the B-loop in pre-fusion state contacts the C-terminal fragment of this protein with more hydrophobic interactions as compared to AHA2. This structure illustrates the distinct stabilization strategy employed by BHA2 to form a post-fusion state that resembles that for AHA2. These studies will further the understanding of BHA with respect to its role in receptor binding ability and fusion.
Functional and Structural Studies of Respiratory Syncytial Virus Fusion (RSV F) Protein and Neutralizing Antibody
(2018-04-18) Xie, Qingqing; Ma, Jianpeng
Human respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections in newborns, young children, and the elderly; causing significant morbidity and mortality each year. Unfortunately, there is currently no licensed vaccine for prophylaxis of RSV infection, and the therapeutic options are limited. The research and development of efficient RSV vaccine or therapeutic antibody has been a priority globally. RSV fusion (F) glycoprotein on the surface of the particle plays a central role in infection, mediating viral entry into host. It is an almost exclusive target for vaccine design and therapeutic antibody development in this field. RSV F has multiple antigenic sites on the pre-fusion and the post-fusion conformations. In Chapter 2, we characterized a series of monoclonal antibodies. We found a novel one, designated as R4.C6, binds with sub-nanomole affinity to a unique neutralizing site that occupies an intermediate position between antigenic sites II and IV on the globular head. Cryo-EM and 3D image reconstruction at 3.9 Å resolution was used to reveal the interaction of R4.C6 Fab in complex with the RSV F glycoprotein. Three R4.C6 Fab were found to bind to a quaternary epitope across two protomers; both heavy chain and light chain have interactions with site II of one protomer and site IV of the neighboring protomer. These results further our understanding of the antigenic complexity of the F protein and provide new insight into the design of RSV vaccines. The F glycoprotein is structurally complex with multiple conformations. It undergoes conformation shift from prefusion to pre-hairpin intermediate and finally to postfusion state. The combined energies released during multiple conformational rearrangements are used to bring the N- and C-terminal of RSV F1 subunit together, forcing the close contacts of virus and host cell membranes, and finally membrane fusion. In Chapter 3, we performed single particle cryo-EM analysis for another form of RSV F, named as BV2052. The 5 Å resolution density map shows some features that distinguished from the prefusion and postfusion state. BV2052 is in a intermediate state. This helps us understand the fusion mechanism.
Gauss Integral as a structural descriptor of proteins
(2008) Mei, Yuan; Ma, Jianpeng
Protein is one of the most important macromolecules in structural biology. Its three-dimensional structure is uniquely determined by its amino-acid sequence. Due to large number of degrees of freedom and enormous diversity, it is important to seek universal geometric measures to describe the overall structure of proteins. Gauss Integral and its related quantities are shown to be capable of describing important features of protein structures on different levels. On the atomic level, the Gauss Integral provides a simple measure to illustrate the orientational relation between different atom groups. It can be viewed as a generalization of the Ramachandran map, and it also reveals several novel relations between backbone atoms. On the other hand, Gauss Integrals can be treated as a set of collective quantities that capture the overall shape of proteins. By comparing the values of Gauss Integrals, different types of proteins can be classified in accordance with the results of manually classified structure database.
Gene Network Modeling of Cancer Metabolism
(2016-04-20) Yu, Linglin; Ma, Jianpeng
Metabolism plays a crucial role in cellular behaviors and activities. The abnormal metabolism has been proposed to be one of the hallmarks of cancer. Unlike normal cells, cancer cells largely depend on glycolysis to produce energy even in the presence of oxygen, which is referred as the Warburg effect. Recent evidences, however, suggest that oxidative phosphorylation is also required for cancer progression. Yet, the underlying regulatory mechanism of these metabolic modes in cancer cells is still poorly understood. Here we use the computational systems biology approach to establish a theoretical framework for modeling genetic regulation of cancer metabolism. According to experimental evidences, we built a network of both regulatory proteins and metabolites. The network was first coarse-grained to a three-component regulatory circuit composed of HIF-1, AMPK and ROS. Thereafter, we further explored the interplay between the circuit and the metabolic pathways, including glucose oxidation, glycolysis and fatty acid oxidation. By exploring the dynamics of the metabolic circuits, we show that, while normal cells have two stable steady states – an oxidative state (O: low HIF-1, high AMPK) and a Warburg state (W: high HIF-1, low AMPK), cancer cells open an additional hybrid state (W/O: high HIF-1, high AMPK) due to higher mitochondrial ROS production and lower HIF-1 degradation rate. The ‘W/O’ hybrid phenotype contributes to cancer metabolic heterogeneity and plasticity, thus allowing cancer cells to adapt to the changes in tumor microenvironment and to promote cell proliferation and metastasis. Based on the model, we investigated the effectiveness of possible cancer therapies targeting metabolism in reducing the metabolic plasticity and circumventing the hybrid state during the course of treatment. We also discuss the connection of the metabolic hybrid state to EMT and stemness of cancer cells.

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