Automatic Separation and Collection of Cancer-Related Substances from Clinical Samples.

Recently, liquid biopsies have been used to diagnose various diseases, including cancer. Body fluids contain many substances, including cells, proteins, and nucleic acids originating from normal tissues, but some of these substances also originate from the diseased area. The investigation and analysis of these substances in the body fluids play a pivotal role in the diagnosis of various diseases. Therefore, it is important to accurately separate the required substances, and several techniques are developed to be used for this purpose. We have developed a lab-on-a-disc type of device and platform named CD-PRIME. This device is automated and has good results for sample contamination and sample stability. Moreover, it has advantages of a good acquisition yield, a short operation time, and high reproducibility. In addition, depending on the type of disc to be mounted, plasma containing cell-free DNA, circulating tumor cells, peripheral blood mononuclear cells, or buffy coats can be separated. Thus, the acquisition of a variety of materials present in the body fluids can be done for a variety of downstream applications, including the study of omics.

A mass weighted chemical elastic network model elucidates closed form domain motions in proteins.

An elastic network model (ENM), usually Cα coarse-grained one, has been widely used to study protein dynamics as an alternative to classical molecular dynamics simulation. This simple approach dramatically saves the computational cost, but sometimes fails to describe a feasible conformational change due to unrealistically excessive spring connections. To overcome this limitation, we propose a mass-weighted chemical elastic network model (MWCENM) in which the total mass of each residue is assumed to be concentrated on the representative alpha carbon atom and various stiffness values are precisely assigned according to the types of chemical interactions. We test MWCENM on several well-known proteins of which both closed and open conformations are available as well as three α-helix rich proteins. Their normal mode analysis reveals that MWCENM not only generates more plausible conformational changes, especially for closed forms of proteins, but also preserves protein secondary structures thus distinguishing MWCENM from traditional ENMs. In addition, MWCENM also reduces computational burden by using a more sparse stiffness matrix.

UMMS: constrained harmonic and anharmonic analyses of macromolecules based on elastic network models.

UMass Morph Server (UMMS) has been developed for the broad impact on the study of molecular dynamics (MD). The elastic network model (ENM) of a given macromolecule has been proven as a useful tool for analyzing thermal behaviors locally and predicting folding pathways globally. UMMS utilizes coarse-grained ENMs at various levels. These simplifications remarkably save computation time compared with all-atom MD simulations so that one can bring down massive computational problems from a supercomputer to a PC. To improve computational efficiency and physical reality of ENMs, the symmetry-constrained, rigid-cluster, hybrid and chemical-bond ENMs have been developed and implemented at UMMS. One can request both harmonic normal mode analysis of a single macromolecule and anharmonic pathway generation between two conformations of a same molecule using elastic network interpolation at http://biomechanics.ecs.umass.edu/umms.html.

A method for finding candidate conformations for molecular replacement using relative rotation between domains of a known structure.

This paper presents a methodology to obtain candidate conformations of multidomain proteins for use in molecular replacement. For each separate domain, the orientational relationship between the template and the target structure is obtained using standard molecular replacement. The orientational relationships of the domains are then used to calculate the relative rotation between the domains in the target conformation by using pose-estimation techniques from the field of robotics and computer vision. With the angle of relative rotation between the domains as a cost function, iterative normal-mode analysis is used to drive the template structure to a candidate conformation that matches the X-ray crystallographic data obtained for the target conformation. The selection of the correct intra-protein domain orientations from among the many spurious maxima in the rotation function (including orientations obtained from domains in symmetry mates rather than within the same copy of the protein) presents a challenge. This problem is resolved by checking R factors of each domain, measuring the absolute value of relative rotation between domains, and evaluating the cost value after each candidate conformation is driven to convergence with iterative NMA. As a validation, the proposed method is applied to three test proteins: ribose-binding protein, lactoferrin and calcium ATPase. In each test case, the orientation and translation of the final candidate conformation in the unit cell are generated correctly from the suggested procedure. The results show that the proposed method can yield viable candidate conformations for use in molecular replacement and can reveal the structural details and pose of the target conformation in the crystallographic unit cell.

A connection rule for alpha-carbon coarse-grained elastic network models using chemical bond information.

A sparser but more efficient connection rule (called a bond-cutoff method) for a simplified alpha-carbon coarse-grained elastic network model is presented. One of conventional connection rules for elastic network models is the distance-cutoff method, where virtual springs connect an alpha-carbon with all neighbor alpha-carbons within predefined distance-cutoff value. However, though the maximum interaction distance between alpha-carbons is reported as 7 angstroms, this cutoff value can make the elastic network unstable in many cases of protein structures. Thus, a larger cutoff value (>11 angstroms) is often used to establish a stable elastic network model in previous researches. To overcome this problem, a connection rule for backbone model is proposed, which satisfies the minimum condition to stabilize an elastic network. Based on the backbone connections, each type of chemical interactions is considered and added to the elastic network model: disulfide bonds, hydrogen bonds, and salt-bridges. In addition, the van der Waals forces between alpha-carbons are modeled by using the distance-cutoff method. With the proposed connection rule, one can make an elastic network model with less than 7 angstroms distance cutoff, which can reveal protein flexibility more sharply. Moreover, the normal modes from the new elastic network model can reflect conformational changes of a given protein better than ones by the distance-cutoff method. This method can save the computational cost when calculating normal modes of a given protein structure, because it can reduce the total number of connections. As a validation, six example proteins are tested. Computational times and the overlap values between the conformational change and infinitesimal motion calculated by normal mode analysis are presented. Those animations are also available at UMass Morph Server (http://biomechanics.ecs.umass.edu/umms.html).