Biomedical subjects

Amir Assadi

Publications and source records attributed to Amir Assadi.

3 recordsLinked to original sources

PubMed2005

Protein energetic conformational analysis from NMR chemical shifts (PECAN) and its use in determining secondary structural elements.

We present an energy model that combines information from the amino acid sequence of a protein and available NMR chemical shifts for the purposes of identifying low energy conformations and determining elements of secondary structure. The model ("PECAN", Protein Energetic Conformational Analysis from NMR chemical shifts) optimizes a combination of sequence information and residue-specific statistical energy function to yield energetic descriptions most favorable to predicting secondary structure. Compared to prior methods for secondary structure determination, PECAN provides increased accuracy and range, particularly in regions of extended structure. Moreover, PECAN uses the energetics to identify residues located at the boundaries between regions of predicted secondary structure that may not fit the stringent secondary structure class definitions. The energy model offers insights into the local energetic patterns that underlie conformational preferences. For example, it shows that the information content for defining secondary structure is localized about a residue and reaches a maximum when two residues on either side are considered. The current release of the PECAN software determines the well-defined regions of secondary structure in novel proteins with assigned chemical shifts with an overall accuracy of 90%, which is close to the practical limit of achievable accuracy in classifying the states.

Algorithms↗

PubMed2005

Probabilistic Identification of Spin Systems and their Assignments including Coil-Helix Inference as Output (PISTACHIO).

We present a novel automated strategy (PISTACHIO) for the probabilistic assignment of backbone and sidechain chemical shifts in proteins. The algorithm uses peak lists derived from various NMR experiments as input and provides as output ranked lists of assignments for all signals recognized in the input data as constituting spin systems. PISTACHIO was evaluated by comparing its performance with raw peak-picked data from 15 proteins ranging from 54 to 300 residues; the results were compared with those achieved by experts analyzing the same datasets by hand. As scored against the best available independent assignments for these proteins, the first-ranked PISTACHIO assignments were 80-100% correct for backbone signals and 75-95% correct for sidechain signals. The independent assignments benefited, in a number of cases, from structural data (e.g. from NOESY spectra) that were unavailable to PISTACHIO. Any number of datasets in any combination can serve as input. Thus PISTACHIO can be used as datasets are collected to ascertain the current extent of secure assignments, to identify residues with low assignment probability, and to suggest the types of additional data needed to remove ambiguities. The current implementation of PISTACHIO, which is available from a server on the Internet, supports input data from 15 standard double- and triple-resonance experiments. The software can readily accommodate additional types of experiments, including data from selectively labeled samples. The assignment probabilities can be carried forward and refined in subsequent steps leading to a structure. The performance of PISTACHIO showed no direct dependence on protein size, but correlated instead with data quality (completeness and signal-to-noise). PISTACHIO represents one component of a comprehensive probabilistic approach we are developing for the collection and analysis of protein NMR data.

Algorithms↗

PubMed2004

Unsupervised clustering algorithm for N-dimensional data.

Cluster analysis is an important tool for classifying data. Established techniques include k-means and k-median cluster analysis. However, these methods require the user to provide a priori estimations of the number of clusters and their approximate location in the parameter space. Often these estimations can be made based on some prior understanding about the nature of the data. Alternatively, the user makes these estimations based on visualization of the data. However, the latter is problematic in data sets with large numbers of dimensions. Presented here is an algorithm that can automatically provide these estimates without human intervention based on the inherent structure of the data set. The number of dimensions does not limit it.

Algorithms↗