Lower-energy conformers search of TPP-1 polypeptide via hybrid particle swarm optimization and genetic algorithm

Low-energy conformation search on biological macromolecules remains a challenge in biochemical experiments and theoretical studies. Finding efficient approaches to minimize the energy of peptide structures is critically needed for researchers either studying peptide-protein interactions or designing peptide drugs. In this study, we aim to develop a heuristic-based algorithm to efficiently minimize a promising PD-L1 inhibiting polypeptide, TPP-1, and build its low-energy conformer pool to advance its subsequent structure optimization and molecular docking studies. Through our study, we find that, using backbone dihedral angles as the decision variables, both PSO and GA can outperform other existing heuristic approaches in optimizing the structure of Met-enkephalin, a benchmarking pentapeptide for evaluating the efficiency of conformation optimizers. Using the established algorithm pipeline, hybridizing PSO and GA minimized TPP-1 structure efficiently and a low-energy pool was built with an acceptable computational cost (a couple days using a single laptop). Remarkably, the efficiency of hybrid PSO-GA is hundreds-fold higher than the conventional Molecular Dynamic simulations running under the force filed. Meanwhile, the stereo-chemical quality of the minimized structures was validated using Ramachandran plot. In summary, hybrid PSO-GA minimizes TPP-1 structure efficiently and yields a low-energy conformer pool within a reasonably short time period. Overall, our approach can be extended to biochemical research to speed up the peptide conformation determinations and hence can facilitate peptide-involved drug development

Discovery of the molecular interactions mediating malaria transmission in the mosquito midgut

Malaria is a worldwide health problem that affects two thirds of the world population and kills approximately one million people annually. Infecting Anopheline mosquitoes is the essential step for malaria transmission. However, the molecular mechanisms of Plasmodium invasion of the mosquito midgut have not been fully elucidated. We identified that the genetic polymorphisms of fibrinogen-related protein 1 (FREP1) gene are significantly associated with Plasmodium falciparum infection in Anopheles gambiae and essential for P. berghei infection in An. gambiae. Moreover, we identified that FREP1 was a tetrameric oligomer and secreted outside of cells. Notably FREP1 bound to the mosquito midgut peritrophic matrix (PM) through direct interaction to Plasmodium ookinetes that invade mosquitoes. Disrupting FREP1 expression by RNAi or blocking endogenous FREP1 by antibodies significantly (p ≤ 0.01) inhibited Plasmodium infection in mosquito midguts. Based on these, we propose that FREP1 mediates Plasmodium invasion of Anopheles midguts. Furthermore, nine P. berghei proteins were identified as candidate FREP1 binding partners (FBP) through pull-down experiments followed by mass spectrometry assays. We cloned these genes and expressed them in insect cells and E. coli. All insect cell-expressed recombinant FBPs interact with FREP1. To test the role of FBPs in malaria transmission, E. coli expressed recombinant proteins were injected into mice to generate polyclonal antibodies. Six FBPs turn out to be strongly immunogenic as evidenced from high specific titers in mouse serum. We will examine activities of these antibodies in inhibiting P. falciparum transmission to An. gambiae in vivo. Besides FREP1-mediated pathway, multiple pathways are hypothesized to involving malaria transmission. Through computational approaches based on protein sequences and gene expression profiles, 95 An. gambiae genes were selected, and 15 of them were cloned and expressed in insect cells. Ten of the recombinant proteins bound to Plasmodium parasites. RNA interference assays confirmed four related to P. falciparum transmission to mosquitoes. Collectively, mosquito midgut FREP1, secreted from the epithelium and functioning as tetramers, mediates Plasmodium invasion via anchoring ookinetes to the mosquito PM and facilitates parasite penetration into the epithelium. Our newly identified mosquito midgut proteins including FREP1 and parasitic binding partners will enable us to limit malaria transmission with novel intervention strategies

A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.Comment: 15 pages, 6 figure

Review on stationary phases and coating methods of MEMs gas chromatography columns

Gas chromatography (GC) is an important and widely used technique for separation and analysis in the field of analytical chemistry. Micro gas chromatography has been developed in response to the requirement for on-line analysis and on-site analysis. At the core of micro gas chromatography, microelectromechanical systems (MEMs) have the advantages of small size and low power consumption. This article introduces the stationary phases of micro columns in recent years, including polymer, carbon materials, silica, gold nanoparticles, inorganic adsorbents and ionic liquids. Preparation techniques ranging from classical coating to unusual sputtering of stationary phases are reviewed. The advantages and disadvantages of different preparation methods are analyzed. The paper introduces the separation characteristics and application progress of MEMs columns and discusses possible developments

Generalized particle domain method: An extension of material point method generates particles from the CAD files

In this paper, a generalized particle domain method (GPDM) is proposed and developed within the framework of the convected particle domain interpolation method. This new method generates particles directly from non-uniform rational B-spline (NURBS)-based CAD file of a continuum body. The particle domain corresponds to a NURBS element even for trimmed elements of solids with complex geometries. The shape functions and the gradient of shape functions are evaluated using NURBS basis functions to map material properties between particles and grid nodes. It approves that this proposed GPDM can track the domain of particles accurately and avoid the issue of cell-crossing instability. Several numerical examples are presented to demonstrate the high performance of this proposed new particle domain method. It is shown that the results obtained using the proposed GPDM are consistent with the experimental data reported in the literature. Further development of the generalized particle domain method can provide a link to the material point method and isogeometric analysis

Genome-block expression-assisted association studies discover malaria resistance genes in Anopheles gambiae.

The malaria parasite-resistance island (PRI) of the African mosquito vector, Anopheles gambiae, was mapped to five genomic regions containing 80 genes, using coexpression patterns of genomic blocks. High-throughput sequencing identified 347 nonsynonymous single-nucleotide polymorphisms within these genes in mosquitoes from malaria-endemic areas in Kenya. Direct association studies between nonsynonymous single-nucleotide polymorphisms and Plasmodium falciparum infection identified three naturally occurring genetic variations in each of three genes (An. gambiae adenosine deaminase, fibrinogen-related protein 30, and fibrinogen-related protein 1) that were associated significantly with parasite infection. A role for these genes in the resistance phenotype was confirmed by RNA interference knockdown assays. Silencing fibrinogen-related protein 30 increased parasite infection significantly, whereas ablation of fibrinogen-related protein 1 transcripts resulted in mosquitoes nearly free of parasites. The discovered genes and single-nucleotide polymorphisms are anticipated to be useful in the development of tools for malaria control in endemic areas in Africa

Automated affinity selection for rapid discovery of peptide binders

This work reports an automated affinity selection-mass spectrometry (AS-MS) approach amenable to both de novo peptide binder discovery and affinity maturation of known binders in a high-throughput and selective manner.</jats:p