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Biomedical subjects

Vadim Alexandrov

Publications and source records attributed to Vadim Alexandrov.

6 recordsLinked to original sources
PubMed2006

Protein self-organization patterns in dried serum reveal changes in B-cell disorders.

BACKGROUND: Detection of serum monoclonal proteins is a common laboratory analysis used in the evaluation of patients with B-cell disorders. Since many individuals with elevated immunoglobulin have no symptoms, it is important to have simple methods for initial screening of patients with suspected B-cell disorders. METHODS: Samples of serum from healthy donors and from patients with elevated immunoglobulin levels were tested using a technology named Droplet MicroChromatography (DMC). DMC was developed at Artann Laboratories (West Trenton, New Jersey, USA) for the rapid assessment of changes in the composition of serum. DMC is based on the dynamics of the sediment pattern formation during drying of a fluid microdroplet. RESULTS: Results of this pilot study confirm the hypothesis that the pattern formation created by drying droplets of serum would differ between normal samples and those containing monoclonal proteins. Reproducible differences in the patterns formed by the two types of specimens are shown. Strong correlation between abnormally elevated levels of immunoglobulins in the serum of myeloma patients and the patterns formed by drying droplets of serum indicates that the DMC technique may be suitable for semi-quantitative analysis of serum samples. We also demonstrate that computer identification of the drying droplet structure and dynamics is a tractable issue. CONCLUSIONS: DMC has significant diagnostic potential and can serve as a basis for development of a simple, rapid, and inexpensive method for initial screening of patients suspected of having multiple myeloma and other pathologies of lymphoid origin that are associated with the overproduction of monoclonal immunoglobulins. The DMC test requires only approximately 1 microL of serum and could therefore be performed in any facility where it is safe to work with serum.

B-Lymphocytes↗
PubMed2005

Normal modes for predicting protein motions: a comprehensive database assessment and associated Web tool.

We carry out an extensive statistical study of the applicability of normal modes to the prediction of mobile regions in proteins. In particular, we assess the degree to which the observed motions found in a comprehensive data set of 377 nonredundant motions can be modeled by a single normal-mode vibration. We describe each motion in our data set by vectors connecting corresponding atoms in two crystallographically known conformations. We then measure the geometric overlap of these motion vectors with the displacement vectors of the lowest-frequency mode, for one of the conformations. Our study suggests that the lowest mode contains useful information about the parts of a protein that move most (i.e., have the largest amplitudes) and about the direction of this movement. Based on our findings, we developed a Web tool for motion prediction (available from http://molmovdb.org/nma) and apply it here to four representative motions--from bacteriorhodopsin, calmodulin, insulin, and T7 RNA polymerase.

Databases, Protein↗
PubMed2004

Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures.

BACKGROUND: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction. Thus far, however, they have been confined to representing 1D sequence (or the aspects of structure that could be represented by character strings). RESULTS: We develop an HMM formalism that explicitly uses 3D coordinates in its match states. The match states are modeled by 3D Gaussian distributions centered on the mean coordinate position of each alpha carbon in a large structural alignment. The transition probabilities depend on the spread of the neighboring match states and on the number of gaps found in the structural alignment. We also develop methods for aligning query structures against 3D HMMs and scoring the result probabilistically. For 1D HMMs these tasks are accomplished by the Viterbi and forward algorithms. However, these will not work in unmodified form for the 3D problem, due to non-local quality of structural alignment, so we develop extensions of these algorithms for the 3D case. Several applications of 3D HMMs for protein structure classification are reported. A good separation of scores for different fold families suggests that the described construct is quite useful for protein structure analysis. CONCLUSION: We have created a rigorous 3D HMM representation for protein structures and implemented a complete set of routines for building 3D HMMs in C and Perl. The code is freely available from http://www.molmovdb.org/geometry/3dHMM, and at this site we also have a simple prototype server to demonstrate the features of the described approach.

Algorithms↗
PubMed2002

Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic.

We investigated protein motions using normal modes within a database framework, determining on a large sample the degree to which normal modes anticipate the direction of the observed motion and were useful for motions classification. As a starting point for our analysis, we identified a large number of examples of protein flexibility from a comprehensive set of structural alignments of the proteins in the PDB. Each example consisted of a pair of proteins that were considerably different in structure given their sequence similarity. On each pair, we performed geometric comparisons and adiabatic-mapping interpolations in a high-throughput pipeline, arriving at a final list of 3,814 putative motions and standardized statistics for each. We then computed the normal modes of each motion in this list, determining the linear combination of modes that best approximated the direction of the observed motion. We integrated our new motions and normal mode calculations in the Macromolecular Motions Database, through a new ranking interface at http://molmovdb.org. Based on the normal mode calculations and the interpolations, we identified a new statistic, mode concentration, related to the mathematical concept of information content, which describes the degree to which the direction of the observed motion can be summarized by a few modes. Using this statistic, we were able to determine the fraction of the 3,814 motions where one could anticipate the direction of the actual motion from only a few modes. We also investigated mode concentration in comparison to related statistics on combinations of normal modes and correlated it with quantities characterizing protein flexibility (e.g., maximum backbone displacement or number of mobile atoms). Finally, we evaluated the ability of mode concentration to automatically classify motions into a variety of simple categories (e.g., whether or not they are "fragment-like"), in comparison to motion statistics. This involved the application of decision trees and feature selection (particular machine-learning techniques) to training and testing sets derived from merging the "list" of motions with manually classified ones.

Databases, Protein↗