Biology subjects

Ozsan, A.

Publications and source records attributed to Ozsan, A..

3 recordsLinked to original sources

bioRxiv2026

DynaPIN: A Tool for Characterizing Dynamic Protein Interfaces

Static structural models often fail to capture the dynamic mechanisms of protein interactions. To address this, we introduce DynaPIN, an open-source pipeline for extracting dynamic interface fingerprints from molecular simulations. DynaPIN unifies quality control metrics, interface prediction accuracy assessment, and atomistic interaction analysis into a single automated workflow, accessible at https://github.com/CSB-KaracaLab/DynaPIN. A key feature is our interface-specific analysis centered on a Dynamic Interface definition, which classifies residues based on the persistence of their interaction status over the simulation. We applied DynaPIN to representative rigid, medium, and difficult targets from the DynaBench dataset, an MD simulation resource for Docking Benchmark 5.5. Our results show that interface flexibility diverges from static accuracy classifications established in Docking Benchmark 5.5, as explored before. All in all, by providing standardized, frame-resolved outputs, DynaPINs aim is to facilitate mechanistic studies and generate standardized unbiased data for future dynamics-aware artificial intelligence models.

bioinformatics↗

bioRxiv2024

Towards a greener AlphaFold2 protocol for protein complex modeling: Insights from CAPRI Round 55

In the 55th round of CAPRI, we used enhanced AlphaFold2 (AF2) sampling and data-driven docking. Our AF2 protocol relies on Wallners massive sampling approach, which combines different AF2 versions and sampling parameters to produce thousands of models per target. For T231 (an antibody peptide complex) and T232 (PP2A:TIPRL complex), we employed a 50-fold reduced MinnieFold sampling and a custom ranking approach, leading to a top-ranking medium prediction in both cases. For T233 and T234 (two antibody bound MHC I complexes), we followed data-driven docking, which did not lead to an acceptable model. Our post-CAPRI55 analysis showed that if we would have used our MinnieFold approach on T233 and T234, we could have submitted a medium-quality model for T233 as well. In the scoring challenge, we utilized the scoring function of FoldX, which was effective in selecting acceptable models for T231 and medium quality models for T232. Our success, especially in predicting and ranking a medium quality model for T231 and potentially for T233 underscores the feasibility of green and accurate enhanced AF2 sampling in antibody complex prediction.

bioinformatics↗

bioRxiv2020

Lab-Based and Self-Reported Indices of Fitness Show Lowered Fitness and Insight into Fitness in Individuals at Clinical High Risk for Psychosis

IntroductionExercise is a promising intervention for clinical high-risk for psychosis (CHR) populations, who have attenuated positive symptoms, but evidence suggests that these youth may require tailored exercise interventions. Presently, the scope of the problem is unknown, as these youth may not be reliable reporters on fitness. This issue is compounded by the fact that there have been no investigations that utilized a formal fitness assessment in this critical population. The present study aims to determine the level of fitness in CHR youth with lab-based measures, test how effectively self-report measures characterize objective fitness indices, and explore clinical factors that may be interrupting reliable self-report-an important tool if these interventions are to be taken to scale. MethodsForty CHR individuals completed an exercise survey and lab-based indices of fitness (i.e., VO2max and BMI). Forty healthy volunteers completed lab indices of fitness and a structured clinical interview ruling out the presence of psychiatric illness. ResultsCHR youth showed greater BMI and lower VO2max compared to healthy volunteers. In the CHR group, abstract self-report items (perceived fitness) did not reflect lab indices of fitness, whereas specific exercise behaviors (intensity of exercise) showed stronger correlations with laboratory-based fitness measurements. Exploratory analyses suggested that positive symptoms involving grandiosity, and negative symptoms such as avolition, correlated with discrepancy between self-perception and laboratory findings of fitness. DiscussionResults suggest that CHR individuals are less objectively fit than matched controls, and that it will be important to consider unique population characteristics when weighing self-report data.

physiology↗