Biology subjects

Kozakov, D.

Publications and source records attributed to Kozakov, D..

13 recordsLinked to original sources

bioRxiv2026

Differential Expression of TKS4 Isoforms and Their Role in Cellular Processes in Breast Cancer

The scaffold protein TKS4 plays a role in the development of several cancers. Alternative splicing of the TKS4 gene generates two isoforms, TKS4L and TKS4b; however, their distinct expression patterns and functional roles have not yet been characterized. We have shown that TKS4 isoforms were differentially expressed across human cell lines and breast cancer (BC) tumor samples. Both TKS4L and TKS4L/TKS4b mRNA ratios were significantly altered in tumors compared with adjacent tissues. We identified six novel binding SH3-domain-containing partners for TKS4L, none of which interact with TKS4b, suggesting their functional differences. Tyrosine phosphorylation of both isoforms was induced by Src(Y527F) kinase overexpression, enabling binding to the SH2 domains of signaling proteins. Interestingly, TKS4b significantly accumulated in the nucleus, while TKS4L was primarily present in the cytosol in MCF-7 cells. TKS4b overexpression enhanced MCF-7 cell migration. Both TKS4 isoforms exhibit oncogenic properties by promoting epithelial-mesenchymal transition in BC cells, highlighting their potential as targets for therapeutic intervention.

molecular biology↗

bioRxiv2025

CDR-aware masked language models for pairedantibodies enable state-of-the-art bindingprediction

BackgroundTherapeutic antibodies are a leading class of biologics, yet their unique architecture poses challenges for computational modeling. Each antibody comprises paired heavy and light variable domains with conserved framework regions that maintain structure and hypervariable complementarity-determining regions (CDRs) that directly contact antigens. This functional asymmetry, where CDRs determine binding specificity while frameworks provide scaffolding, suggests that region-aware training strategies could yield superior representations. Existing protein language models treat all regions uniformly, potentially missing critical features present in CDRs. MethodsWe developed a region-aware pretraining strategy for paired variable domain sequences using two protein language models: a 3 billion parameter model (ESM2) and a compact 600 million parameter model (ESM C). We compared three masking approaches: uniform whole-chain masking, CDR-focused masking, and a hybrid strategy. Final models were trained on over 1.6 million paired antibody sequences and evaluated on binding affinity datasets with over 90,000 antibody variants across six antigens, including single-mutant panels and combinatorial libraries. ResultsHere we show that CDR-focused training produces embeddings with superior predictive performance for antibody-antigen binding. Our approach achieves up to 27% improvements in binding affinity prediction compared to benchmarked antibody models. Remarkably, training exclusively on paired sequences proves sufficient; pretraining on billions of unpaired sequences provides no measurable benefit. Our compact model matches or exceeds larger antibody-specific baselines. ConclusionsThese findings establish that prioritizing paired sequences with CDR-aware supervision over scale and complex training schemes achieves both computational efficiency and predictive accuracy, providing a practical framework for next generation antibody language models.

bioinformatics↗

bioRxiv2025

Analysis of the phosphorylation networks characterizing distinct phenotypic states in glioblastoma cell Populations

Cancer cells, including the most aggressive brain cancer, glioblastoma, can exhibit a high degree of phenotypic plasticity. However, it is not well understood whether and how distinct phenotypic states might be associated with or driven by specific signaling processes. Here, we use a novel approach to the identification of phenotype-specific signaling networks in cells undergoing the Go-or-Grow switch in populations of GBM cell lines. We find that the transition to invasive spread may be associated with the onset of DNA damage response, cell cycle arrest, and activation of the AKT-mTOR signaling. We reconstructed the large-scale signaling networks, revealing integration of diverse pathways and possible feedback interactions mediating phenotypic stability. We further show that phosphorylation outcomes may be preferentially mediated by 14-3-3 proteins, previously implicated in stress response and control of the cell cycle. Finally, we demonstrate that the phenotype-specific phospho-site signatures have predictive power for disease-free patient survival. This study paves the way for a more comprehensive understanding of phenotypic plasticity in GBM and other aggressive cancers.

systems biology↗

bioRxiv2025

Identification of actionable targeted protein degradation effector sites through Site-specific Ligand Incorporation-induced Proximity (SLIP)

Targeted protein degradation (TPD) is a rapidly emerging and potentially transformative therapeutic modality. However, the large majority of >600 known ubiquitin ligases have yet to be exploited as TPD effectors by proteolysis-targeting chimeras (PROTACs) or molecular glue degraders (MGDs). We report here a chemical-genetic platform, Site-specific Ligand Incorporation-induced Proximity (SLIP), to identify actionable ("PROTACable") sites on any potential effector protein in intact cells. SLIP uses genetic code expansion (GCE) to encode copper-free "click" ligation at a specific effector site in intact cells, enabling in situ formation of a covalent PROTAC-effector conjugate against a target protein of interest (POI). Modification at actionable effector sites drives degradation of the targeted protein, establishing the potential of these sites for TPD. Using SLIP, we systematically screened dozens of sites across E3 ligases and E2 enzymes from diverse classes, identifying multiple novel potentially PROTACable effector sites which are competent for TPD. SLIP adds a powerful approach to the proximity-induced pharmacology (PIP) toolbox, enabling future effector ligand discovery to fully enable TPD, and other emerging PIP modalities.

molecular biology↗

bioRxiv2024

Phospho-Tune: Enhanced Structural Modeling of Phosphorylated Protein Interactions

In computational biology, accurate prediction of phosphopeptide-protein complex structures is essential for understanding cellular functions and advancing drug discovery and personalized medicine. While AlphaFold has significantly improved protein structure prediction, it faces accuracy challenges in predicting structures of complexes involving phosphopeptides possibly due to structural variations introduced by phosphorylation in the peptide component. Our study addresses this limitation by refining AlphaFold to improve its accuracy in modeling these complex structures. We employed weighted metrics for a comprehensive evaluation across various protein families. The enhanced model notably outperforms the original AlphaFold, showing a substantial increase in the weighted average local distance difference test (lDDT) scores for peptides: from 52.74 to 76.51 in the Top 1 model and from 56.32 to 77.91 in the Top 5 model. These advancements not only deepen our understanding of the role of phosphorylation in cellular signaling but also have extensive implications for biological research and the development of innovative therapies.

bioinformatics↗

bioRxiv2023

MHC-Fine: Fine-tuned AlphaFold for Precise MHC-Peptide Complex Prediction

The precise prediction of Major Histocompatibility Complex (MHC)-peptide complex structures is pivotal for understanding cellular immune responses and advancing vaccine design. In this study, we enhanced AlphaFolds capabilities by fine-tuning it with a specialized dataset comprised by exclusively high-resolution MHC-peptide crystal structures. This tailored approach aimed to address the generalist nature of AlphaFolds original training, which, while broad-ranging, lacked the granularity necessary for the high-precision demands of MHC-peptide interaction prediction. A comparative analysis was conducted against the homology-modeling-based method Pandora [13], as well as the AlphaFold multimer model [8]. Our results demonstrate that our fine-tuned model outperforms both in terms of RMSD (median value is 0.65 [A]) but also provides enhanced predicted lDDT scores, offering a more reliable assessment of the predicted structures. These advances have substantial implications for computational immunology, potentially accelerating the development of novel therapeutics and vaccines by providing a more precise computational lens through which to view MHC-peptide interactions.

bioinformatics↗

bioRxiv2022

Improved Predictions Of MHC-Peptide Binding Using Protein Language Models

AO_SCPLOWBSTRACTC_SCPLOWMajor histocompatibility complex (MHC) molecules bind to peptides from exogenous antigens, and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system [1]. Predicting whether a given peptide will bind to the MHC is an important step in the above process, motivating the introduction of many computational approaches. NetMHCPan [2], a pan-specific model predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using a shallow neural network. The successful results of AI methods, especially Natural Language Processing (NLP-based) pretrained models in various applications including protein structure determination, motivated us to explore their use in this problem as well. Specifically, we considered fine-tuning these large deep learning models using as dataset the peptide-MHC sequences. Using standard metrics in this area, and the same training and test sets, we show that our model outperforms NetMHCpan4.1 which has been shown to outperform all other earlier methods [2].

molecular biology↗

bioRxiv2021

Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

It has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.

bioinformatics↗

bioRxiv2021

A multi-tiered map of EMT defines major transition points and identifies vulnerabilities

Epithelial to mesenchymal transition (EMT) is a complex cellular program proceeding through a hybrid E/M state linked to cancer-associated stemness, migration and chemoresistance. Deeper molecular understanding of this dynamic physiological landscape is needed to define events which regulate the transition and entry into and exit from the E/M state. Here, we quantified >60,000 molecules across ten time points and twelve omic layers in human mammary epithelial cells undergoing TGF{beta}-induced EMT. Deep proteomic profiles of whole cells, nuclei, extracellular vesicles, secretome, membrane and phosphoproteome defined state-specific signatures and major transition points. Parallel metabolomics showed metabolic reprogramming preceded changes in other layers, while single-cell RNA sequencing identified transcription factors controlling entry into E/M. Covariance analysis exposed unexpected discordance between the molecular layers. Integrative causal modeling revealed co-dependencies governing entry into E/M that were verified experimentally using combinatorial inhibition. Overall, this dataset provides an unprecedented resource on TGF{beta} signaling, EMT and cancer.

systems biology↗

bioRxiv2020

A simple technique to classify diffraction data from dynamic proteins according to individual polymorphs

One often observes small but measurable differences in diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and potentially reflect the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step to extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). State changes may be expressed as changes in morphology, so that a subset of the polystates may be observed as polymorphs. After mixed-state data are deliberately or inadvertently measured, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here we address this problem using a simple multi-factor clustering approach that classifies each data set using independent observables in order to assign each data set to the correct location in conformation space. We illustrate this method using two independent observables (unit cell constants and intensities) to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. We observe that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.

molecular biology↗

bioRxiv2020

The N34S mutation of SPINK1 may impact the kinetics of trypsinogen activation to cause early trypsin release in the pancreas

The N34S variant of the trypsin inhibitor SPINK1 is the clinically most significant risk factor for chronic pancreatitis, but the underlying molecular mechanism could not be identified. Molecular dynamics simulations and docking of the generated conformational ensemble of SPINK1 to trypsin show that the mutation reduces the fraction of conformations that can directly participate in productive association, thereby reducing the association rate. The small change is difficult to detect by measuring the kinetics of SPINK1 binding to trypsin. However, kinetic modeling reveals that even a small change in the inhibition rate affects the trypsinogen to trypsin conversion rate at the early stage of the reaction when the trypsin concentration is very low, and the impact is substantially amplified by the autocatalytic mechanism of the conversion. Thus, the slightly reduced inhibition rate shortens the delay in the activation of trypsin release, which is therefore occurs within the pancreas.

biophysics↗

bioRxiv2019

Improved Modeling of Peptide-Protein Binding through Global Docking and Accelerated Molecular Dynamics Simulations

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3 [A] - 4.8 [A], being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6 [A] - 2.7 [A], yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.

biophysics↗

bioRxiv2017

High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock

Peptide-protein interactions contribute a significant fraction of the protein-protein interactome. Accurate modeling of these interactions is challenging due to the vast conformational space associated with interactions of highly flexible peptides with large receptor surfaces. To address this challenge we developed a fragment based high-resolution peptide-protein docking protocol. By streamlining the Rosetta fragment picker for accurate peptide fragment ensemble generation, the PIPER docking algorithm for exhaustive fragment-receptor rigid-body docking and Rosetta FlexPepDock for flexible full-atom refinement of PIPER docked models, we successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy. Validation on a representative set of solved peptide-protein complex structures demonstrates the accuracy and robustness of our approach, and opens up the way to high-resolution modeling of many more peptide-protein interactions and to the detailed study of peptide-protein association in general. PIPER-FlexPepDock is freely available to the academic community as a server at http://piperfpd.furmanlab.cs.huji.ac.il.

bioinformatics↗