Author: jlolds

Native Angeleno, professor of Neuroscience and Public Policy at George Mason University

The Mid-Term Elections

As loyal readers know, this blog is strictly non-political (although we do advocate for good science), but without question the mid-term election results will have secondary and tertiary effects that will be felt in the Federal R&D budget. At a macro-level, this is a non-partisan issue because Federal R&D comes out of the discretionary part of the budget (as opposed to mandatory expenditures such as Medicaid and Medicare). As the overall budget gets squeezed by the deficit (and the need to pay more and more on the federal debt), only the discretionary side of the budget is left to cut–at least without a major overhaul of so-called “third rail” entitlement spending.

So the upshot is pressure on the science programs we love to advocate for–such as the National Institutes of Health or the National Science Foundation. It will be interesting to see how the new GOP leadership in the House decides to view these programs. The last time they were in control, after the 1994 elections, NIH did quite well.

We'll have to stay tuned….

Monday Routine at Krasnow–the Seminar

I rarely delve into the day to day here at the Institute, but, mostly to give a flavor of the scientific program, here goes:

Please join us for the next Krasnow Monday Seminar TODAY
Refreshments will be served at 3:30pm.  Come chat with colleagues and like-minded researchers and students prior to the talk at 4pm.

SPEAKER:  Iosif Vaisman,
Professor and Director of the Ph.D. Program,
Department of Bioinformatics and Computational Biology,
George Mason University

Protein function analysis using computational mutagenesis:

Proteins exhibit a wide range of functional consequences upon mutation.
Accurate predictive models for the impact of single amino acid
substitutions on protein stability and activity provide important insights
into protein structure and function. Such models are also valuable for the
design and engineering of new proteins. This talk will focus on a
computational mutagenesis technique based on a four-body, knowledge-based,
statistical contact potential and machine learning methods. For any
mutation due to a single amino acid replacement in a protein, the method
provides an empirical normalized measure of the ensuing environmental
perturbation occurring at every residue position. The predictive
mutagenesis models are evaluated using large training sets of mutants
derived from diverse proteins that have been experimentally studied and
described. These predictive models are either comparable to, or in many
cases signicantly outperform, alternative approaches.

Hat tip to Carolyn Payne and our support staff…