Browsing by Author "Onuchic, José"

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    Antimicrobial Peptides Activity and Efficacy Prediction by Stochastic Models and Machine Learning Methods
    (2024-04-25) Nguyen, Thao; Kolomeisky, Anatoly; Onuchic, José; Tabor, Jeffrey
    The development of new antimicrobial drugs is becoming more urgent than ever due to the rapid emergence of antibiotic resistance and limitations in bacteria targets. A promising alternative that received considerable scientific attention is antimicrobial peptides (AMPs), also known as host defense peptides. In this work, we aim to facilitate the design of more effective peptides using computational tools by solving the following two main challenges in the field. First, the underlying microscopic mechanisms of how AMPs interact with bacteria and other pathogens remain inadequately understood. Second, the infinite possibilities in engineering new peptides is a time-consuming task. We developed a theoretical framework for the interactions of AMPs and bacteria on the single-cell and population levels. We also investigated the effect of AMP cooperativity on efficacy as measured by minimal inhibitory concentrations (MIC), fractional inhibitory concentrations (FIC), and our acceleration parameter R by looking at cases with 1, 2, 3, and eventually an arbitrary number m types of AMPs. Our results explained the broad concentration spectrum where different types of AMP operate more optimally, offering a mechanistic explanation of the bacterial clearance dynamics and AMP cooperativity mechanisms. Increasing the number of AMP components in a mixture while keeping the total amount fixed enhances their synergistic activities, and strong cooperativity can be achieved for weak intermolecular interactions, providing a qualitative measure for the degree of cooperativity applicable in natural systems. We also used feature selection methods to build our machine learning pipeline to extract features that make peptides antimicrobial. This model produced decent accuracy with manual hyperparameter tuning, and the results can be applied to engineer better AMPs.
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    Construction of an Effective Landscape for Multistate Genetic Switches
    (American Physical Society, 2014) Lu, Mingyang; Onuchic, José; Ben-Jacob, Eshel; Center for Theoretical Biological Physics
    Multistate genetic switches play a crucial role during embryonic development and tumorigenesis. An archetypical example is the three-way switch regulating epithelial-hybrid-mesenchymal transitions. We devise a special WKB-based approach to investigate white Gaussian and shot noise effects on three-way switches, and construct an effective landscape in good quantitative agreement with stochastic simulations. This approach allows efficient analytical or numerical calculation of the landscape contours, the optimal path, and the state relative stability for general multicomponent multistate switches.
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    Data Driven Modeling of Proteins
    (2019-03-20) Chen, Justin; Clementi, Cecilia; Onuchic, José
    Proteins are tiny molecular machines that perform the vast majority of the functions in living cells. In order for the protein to perform its function, it has to be able to fold from a disordered coil into a specific compact structure. Two new computational methods are developed that take advantage of the large amount of data generated in both experiments and computer simulations in order to better understand how proteins work. The first method (pyODEM) improves the modeling of proteins on the global scale, while a second method (pyFrustration) probes the protein's local frustration that might impede the folding process. Use of these methods allows us to construct more dynamically accurate protein models and improves our understanding of how a protein folds and performs its function.
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    Turning Oscillations Into Opportunities: Lessons from a Bacterial Decision Gate
    (Nature Publishing Group, 2013) Schultz, Daniel; Lu, Mingyang; Stavropoulos, Trevor; Onuchic, José; Ben-Jacob, Eshel
    Sporulation vs. competence provides a prototypic example of collective cell fate determination. The decision is performed by the action of three modules: 1) A stochastic competence switch whose transition probability is regulated by population density, population stress and cell stress. 2) A sporulation timer whose clock rate is regulated by cell stress and population stress. 3) A decision gate that is coupled to the timer via a special repressilator-like loop. We show that the distinct circuit architecture of this gate leads to special dynamics and noise management characteristics: The gate opens a time-window of opportunity for competence transitions during which it generates oscillations that are turned into a chain of transition opportunities – each oscillation opens a short interval with high transition probability. The special architecture of the gate also leads to filtering of external noise and robustness against internal noise and variations in the circuit parameters.