Biology subjects

Barlas, A. B.

Publications and source records attributed to Barlas, A. B..

6 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↗

bioRxiv2025

Exploring the Potential of AlphaFold Distograms for Flexibility Assignment in Cryo-EM Maps

AlphaFold models provide static structural predictions, limiting their use in interpreting flexible regions in low-resolution cryo-EM maps. Here, we assess whether AlphaFold-generated distograms can instead reveal conformational flexibility, focusing on binding-induced hinge motions. For this, we examined the key metabolic AK2/AIFM1 complex, where molecular dynamics and cryo-EM confirm a hinge motion in AK2 upon binding. Notably, this motion is captured in the AlphaFold2/3 distograms of apo AK2, even though it is absent in the predicted structures. By extending our analysis to other systems, we demonstrate that distograms may offer a valuable, model-independent method for interpreting ambiguous regions in cryo-EM maps.

bioinformatics↗

bioRxiv2024

An NADH-controlled gatekeeper of ATP synthase

ATP fuels crucial cellular processes and is obtained mostly by oxidative phosphorylation (OXPHOS) at the inner mitochondrial membrane. While significant progress has been made in mechanistic understanding of ATP production, critical aspects surrounding its substrate supply logistics are poorly understood. We identify an interaction between mitochondrial apoptosis-inducing factor 1 (AIFM1) and adenylate kinase 2 (AK2) as gatekeeper of ATP synthase. This interaction is NADH-dependent and influenced by glycolysis, linking it to the cells metabolic state. Genetic interference with AIFM1/AK2 association impedes the ability of Caenorhabditis elegans animals to handle altered metabolic rates and nutrient availability. Together, the results imply AIFM1 as a cellular NADH sensor, placing AK2 next to the OXPHOS complexes for local ADP regeneration as the substrate for ATP synthesis. This metabolic signal relay balances ATP synthase substrate supply against ATP conservation, enabling cells to adapt to fluctuating energy availability, with possible implications for AIFM1-related mitochondrial diseases. HighlightsO_LIDiscovery of AIFM1/AK2 interaction as a gatekeeper of mitochondrial ATP synthase C_LIO_LIAIFM1/AK2 interaction is NADH-dependent and linked to the cells metabolic state C_LIO_LIDisrupting AIFM1/AK2 impairs metabolic adaptation in Caenorhabditis elegans C_LIO_LIAIFM1 as NADH sensor, influencing ATP synthesis with mitochondrial disease implications C_LI

biochemistry↗

bioRxiv2024

A direct readout mechanism regulates the sequence selectivity of human de novo DNA methyltransferases

DNMT3A and DNMT3B are closely related DNA methyltransferases that catalyze de novo CpG methylation yet exhibit distinct preferences for flanking sequences. Despite sharing 91% sequence similarity within their catalytic domains, these paralogs show non-overlapping genomic targeting and divergent biological roles. To uncover the mechanistic basis of this specificity, we performed 16{micro}s of all-atom molecular dynamics simulations on DNMT3A and DNMT3B complexes bound to CpG substrates with varied +2 flanking bases (i.e., CGX). To resolve their base- and shape-readout mechanisms at atomistic detail, we introduce a Comparative Dynamics Analysis (CDA) framework. Our CDA analysis reveals that DNMT3A relies on a rigid, sequence-specific hydrogen bonding network and shape-constrained electrostatic anchoring, whereas DNMT3B employs a more flexible and distributed interface, allowing broader substrate tolerance. This represents the first systematic analysis of shape readout in DNMT3 enzymes and demonstrates how flanking sequence specificity is dynamically encoded by two nearly identical proteins. Our findings not only clarify how closely related DNA-modifying enzymes diverge in recognition strategies, but also lay the foundation for future efforts to engineer paralog-specific protein-DNA interactions.

biophysics↗

bioRxiv2022

Benchmarking the Widely Used Structure-based Binding Affinity Predictors on the Spike-ACE2 Deep Mutational Interaction Set

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across Receptor Binding Domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). Also, when we simplified the prediction problem to a binary classification, i.e., whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we also demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/

bioinformatics↗

bioRxiv2021

Differential Histone-DNA Interactions Dictate Nucleosome Recognition of the Pioneer Transcription Factor Sox

Pioneer transcription factors (PTFs) have the remarkable ability to directly bind to chromatin for stimulating vital cellular processes. In this work, we dissect the universal binding mode of Sox PTF by combining extensive molecular simulations and DNA footprinting techniques. As a result, we show that when Sox consensus DNA is located at the solvent-facing DNA strand, Sox binds to the compact nucleosome without imposing any significant conformational changes. We also reveal that the basespecific Sox:DNA interactions (base reading) and the Sox-induced DNA changes (shape reading) are concurrently required for the sequence-specific DNA recognition. Among different nucleosomal positions, such a specific reading mechanism is satisfied solely at superhelical location 2 (SHL2). While SHL2 acts transparently to Sox binding, SHL4 permits only shape reading, and SHL0 (dyad) allows no reading mechanism. These findings demonstrate for the first time that Sox-based nucleosome recognition is essentially guided by the distinct histone-DNA interactions, permitting varying degrees of DNA flexibility.

biophysics↗