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Kocuk, B.

Publications and source records attributed to Kocuk, B..

2 recordsLinked to original sources

Multiply Perturbed Response to Disclose Allosteric Control of Conformational Change: Application to Fluorescent Biosensor Design

Proteins exhibit remarkable conformational flexibility, enabling precise functional regulation through allostery. A key application of allostery is in the design of protein-based sensors, which detect environmental changes--such as ligand binding or post-translational modifications--and convert these cues into measurable signals (e.g., fluorescence). Here, we investigate a series of ligand-binding proteins that serve as sensing domains in direct-response fluorescent biosensors, wherein ligand binding enhances fluorescence output. We employ a multiple force application approach which we term Multiply Perturbed Response (MPR) to identify "hot spot" residues that drive the conformational transition from an apo (inactive/OFF) to a holo (active/ON) state. We first present two efficient computational approaches to determine residues and forces that maximize the overlap of the observed conformational change. We then determine the overlap maximizer residues for up to five force insertion locations, and we compare them with actual insertion sites used in existing biosensors. This work enhances the utility of linear response theory-based methods in uncovering multiple functionally significant regions that trigger a known conformational change. The approach might prove useful not only in the design of biosensors, or in locating allosteric sites, but may also find applications in offering physics-based collective variables in mapping conformational transition pathways of proteins.

biophysics↗

A Stochastic Programming Approach to the Antibiotics Time Machine Problem

Antibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modelled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics are given. The transition probabilities of a realization of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realizations. The aim is to maximize the expected probability of reaching to the genotype with all unmutated genes given the initial genotype in a predetermined number of transitions, considering the following two sources of uncertainties: i) the randomness in growth rates, ii) the randomness in transition probabilities, which are functions of growth rates. We develop stochastic mixed-integer linear programming and dynamic programming approaches to solve static and dynamic versions of the Antibiotics Time Machine Problem under the aforementioned uncertainties. We adapt a Sample Average Approximation approach that exploits the special structure of the problem and provide accurate solutions that perform very well in an out-of-sample analysis.

evolutionary biology↗