Optoinjection for efficient targeted delivery of a broad range of compounds and macromolecules into diverse cell types.

Efficient delivery of compounds and macromolecules into living cells is essential in many fields including basic research, applied drug discovery, and clinical gene therapy. Unfortunately, current delivery methods, such as cationic lipids and electroporation, are limited by the types of macromolecules and cells that can be employed, poor efficiency, and/or cell toxicity. To address these issues, novel methods were developed based on laser-mediated delivery of macromolecules into cells through optoinjection. An automated high-throughput instrument, the laser-enabled analysis and processing (LEAP) system, was utilized to elucidate and optimize several parameters that influence optoinjection efficiency and toxicity. Techniques employing direct cell irradiation (i.e., targeted to specific cell coordinates) and grid-based irradiation (i.e., without locating individual cells) were both successfully developed. With both techniques, it was determined that multiple, sequential low radiant exposures produced more favorable results than a single high radiant exposure. Various substances were efficiently optoinjected--including ions, small molecules, dextrans, siRNAs (small interfering RNAs), plasmids, proteins, and semiconductor nanocrystals--into numerous cell types. Notably, cells refractory to traditional delivery methods were efficiently optoinjected with lower toxicity. We establish the broad utility of optoinjection, and furthermore, are the first to demonstrate its implementation in an automated, high-throughput manner.

Prediction of deleterious functional effects of amino acid mutations using a library of structure-based function descriptors.

An automated, active site-focused, computational method is described for use in predicting the effects of engineered amino acid mutations on enzyme catalytic activity. The method uses structure-based function descriptors (Fuzzy Functional Forms trade mark or FFFs trade mark ) to automatically identify enzyme functional sites in proteins. Three-dimensional sequence profiles are created from the surrounding active site structure. The computationally derived active site profile is used to analyze the effect of each amino acid change by defining three key features: proximity of the change to the active site, degree of amino acid conservation at the position in related proteins, and compatibility of the change with residues observed at that position in similar proteins. The features were analyzed using a data set of individual amino acid mutations occurring at 128 residue positions in 14 different enzymes. The results show that changes at key active site residues and at highly conserved positions are likely to have deleterious effects on the catalytic activity, and that non-conservative mutations at highly conserved residues are even more likely to be deleterious. Interestingly, the study revealed that amino acid substitutions at residues in close contact with the key active site residues are not more likely to have deleterious effects than mutations more distant from the active site. Utilization of the FFF-derived structural information yields a prediction method that is accurate in 79-83% of the test cases. The success of this method across all six EC classes suggests that it can be used generally to predict the effects of mutations and nsSNPs for enzymes. Future applications of the approach include automated, large-scale identification of deleterious nsSNPs in clinical populations and in large sets of disease-associated nsSNPs, and identification of deleterious nsSNPs in drug targets and drug metabolizing enzymes.

Structure-based active site profiles for genome analysis and functional family subclassification.

In previous work, structure-based functional site descriptors, fuzzy functional forms (FFFs), were developed to recognize structurally conserved active sites in proteins. These descriptors identify members of protein families according to active-site structural similarity, rather than overall sequence or structure similarity. FFFs are defined by a minimal number of highly conserved residues and their three-dimensional arrangement. This approach is advantageous for function assignment across broad families, but is limited when applied to detailed subclassification within these families. In the work described here, we developed a method of three-dimensional, or structure-based, active-site profiling that utilizes FFFs to identify residues located in the spatial environment around the active site. Three-dimensional active-site profiling reveals similarities and differences among active sites across protein families. Using this approach, active-site profiles were constructed from known structures for 193 functional families, and these profiles were verified as distinct and characteristic. To achieve this result, a scoring function was developed that discriminates between true functional sites and those that are geometrically most similar, but do not perform the same function. In a large-scale retrospective analysis of human genome sequences, this profile score was shown to identify specific functional families correctly. The method is effective at recognizing the likely subtype of structurally uncharacterized members of the diverse family of protein kinases, categorizing sequences correctly that were misclassified by global sequence alignment methods. Subfamily information provided by this three-dimensional active-site profiling method yields key information for specific and selective inhibitor design for use in the pharmaceutical industry.