An Interview with Matthew P. Greving, PhD. Interview by Vicki Glaser.

Matthew P. Greving is Chief Scientific Officer at Nextval Inc., a company founded in early 2010 that has developed a discovery platform called MassInsight™.. He received his PhD in Biochemistry from Arizona State University, and prior to that he spent nearly 7 years working as a software engineer. This experience in solving complex computational problems fueled his interest in developing technologies and algorithms related to acquisition and analysis of high-dimensional biochemical data. To address the existing problems associated with label-based microarray readouts, he beganwork on a technique for label-free mass spectrometry (MS) microarray readout compatible with both matrix-assisted laser/desorption ionization (MALDI) and matrix-free nanostructure initiator mass spectrometry (NIMS). This is the core of Nextval's MassInsight technology, which utilizes picoliter noncontact deposition of high-density arrays on mass-readout substrates along with computational algorithms for high-dimensional data processingand reduction.

Nanostructure-initiator mass spectrometry metabolite analysis and imaging.

Nanostructure-Initiator Mass Spectrometry (NIMS) is a matrix-free desorption/ionization approach that is particularly well-suited for unbiased (untargeted) metabolomics. An overview of the NIMS technology and its application in the detection of biofluid and tissue metabolites are presented. (To listen to a podcast about this feature, please go to the Analytical Chemistry multimedia page at pubs.acs.org/page/ancham/audio/index.html .).

Thermodynamic additivity of sequence variations: an algorithm for creating high affinity peptides without large libraries or structural information.

BACKGROUND: There is a significant need for affinity reagents with high target affinity/specificity that can be developed rapidly and inexpensively. Existing affinity reagent development approaches, including protein mutagenesis, directed evolution, and fragment-based design utilize large libraries and/or require structural information thereby adding time and expense. Until now, no systematic approach to affinity reagent development existed that could produce nanomolar affinity from small chemically synthesized peptide libraries without the aid of structural information. METHODOLOGY/PRINCIPAL FINDINGS: Based on the principle of additivity, we have developed an algorithm for generating high affinity peptide ligands. In this algorithm, point-variations in a lead sequence are screened and combined in a systematic manner to achieve additive binding energies. To demonstrate this approach, low-affinity lead peptides for multiple protein targets were identified from sparse random sequence space and optimized to high affinity in just two chemical steps. In one example, a TNF-α binding peptide with K(d) = 90 nM and high target specificity was generated. The changes in binding energy associated with each variation were generally additive upon combining variations, validating the basis of the algorithm. Interestingly, cooperativity between point-variations was not observed, and in a few specific cases, combinations were less than energetically additive. CONCLUSIONS/SIGNIFICANCE: By using this additivity algorithm, peptide ligands with high affinity for protein targets were generated. With this algorithm, one of the highest affinity TNF-α binding peptides reported to date was produced. Most importantly, high affinity was achieved from small, chemically-synthesized libraries without the need for structural information at any time during the process. This is significantly different than protein mutagenesis, directed evolution, or fragment-based design approaches, which rely on large libraries and/or structural guidance. With this algorithm, high affinity/specificity peptide ligands can be developed rapidly, inexpensively, and in an entirely chemical manner.

Discovery of high-affinity protein binding ligands--backwards.

BACKGROUND: There is a pressing need for high-affinity protein binding ligands for all proteins in the human and other proteomes. Numerous groups are working to develop protein binding ligands but most approaches develop ligands using the same strategy in which a large library of structured ligands is screened against a protein target to identify a high-affinity ligand for the target. While this methodology generates high-affinity ligands for the target, it is generally an iterative process that can be difficult to adapt for the generation of ligands for large numbers of proteins. METHODOLOGY/PRINCIPAL FINDINGS: We have developed a class of peptide-based protein ligands, called synbodies, which allow this process to be run backwards--i.e. make a synbody and then screen it against a library of proteins to discover the target. By screening a synbody against an array of 8,000 human proteins, we can identify which protein in the library binds the synbody with high affinity. We used this method to develop a high-affinity synbody that specifically binds AKT1 with a K(d)<5 nM. It was found that the peptides that compose the synbody bind AKT1 with low micromolar affinity, implying that the affinity and specificity is a product of the bivalent interaction of the synbody with AKT1. We developed a synbody for another protein, ABL1 using the same method. CONCLUSIONS/SIGNIFICANCE: This method delivered a high-affinity ligand for a target protein in a single discovery step. This is in contrast to other techniques that require subsequent rounds of mutational improvement to yield nanomolar ligands. As this technique is easily scalable, we believe that it could be possible to develop ligands to all the proteins in any proteome using this approach.

High-throughput screening in two dimensions: binding intensity and off-rate on a peptide microarray.

We report a high-throughput two-dimensional microarray-based screen, incorporating both target binding intensity and off-rate, which can be used to analyze thousands of compounds in a single binding assay. Relative binding intensities and time-resolved dissociation are measured for labeled tumor necrosis factor alpha (TNF-alpha) bound to a peptide microarray. The time-resolved dissociation is fitted to a one-component exponential decay model, from which relative dissociation rates are determined for all peptides with binding intensities above background. We show that most peptides with the slowest off-rates on the microarray also have the slowest off-rates when measured by surface plasmon resonance (SPR).

Feature-level MALDI-MS characterization of in situ-synthesized peptide microarrays.

Characterizing the chemical composition of microarray features is a difficult yet important task in the production of in situ-synthesized microarrays. Here, we describe a method to determine the chemical composition of microarray features, directly on the feature. This method utilizes nondiffusional chemical cleavage from the surface along with techniques from MALDI-MS tissue imaging, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput.