Anja Conev - Academia.edu (original) (raw)

Papers by Anja Conev

Briefings in Bioinformatics, Jul 7, 2023

is a postdoctoral researcher in the Department of Biology and Biochemistry at the University of H... more is a postdoctoral researcher in the Department of Biology and Biochemistry at the University of Houston. His research interests are cancer research, immunotherapy and tumor antigen screening. Hussain Kalavadwala is a PhD student in the Department of Biology and Biochemistry at the University of Houston. His research interests include cell biology, molecular biology and bioinformatics. Martiela V. de Freitas is a postdoctoral researcher in the Department of Biology and Biochemistry at the University of Houston. Her research interests include bioinformatics, genomics, immunology and immunoinformatics. Cecilia Clementi is a professor of computational physics at the Free University of Berlin. She specializes in coarse-grain and multi-scale modelling of biophysical systems to better understand cellular functions. Geancarlo Zanatta is a professor in the Department of Biophysics at Federal University of Rio Grande do Sul (UFRGS). His research focuses on using experimental and computational structural biology to understand pharmacological molecular interactions.

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure de... more Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conform...

Frontiers in Immunology

The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has ... more The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has affected millions of people worldwide. There is constant search for new therapies to either prevent or mitigate the disease. Fortunately, we have observed the successful development of multiple vaccines. Most of them are focused on one viral envelope protein, the spike protein. However, such focused approaches may contribute for the rise of new variants, fueled by the constant selection pressure on envelope proteins, and the widespread dispersion of coronaviruses in nature. Therefore, it is important to examine other proteins, preferentially those that are less susceptible to selection pressure, such as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral response, peptides from its conserved regions can be presented by class I Human Leukocyte Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the increased number of protein ...

Tissue Engineering Part A, 2020

Various material compositions have been successfully used in 3D printing with promising applicati... more Various material compositions have been successfully used in 3D printing with promising applications as scaffolds in tissue engineering. However, identifying suitable printing conditions for new materials requires extensive experimentation in a time and resource-demanding process. This study investigates the use of Machine Learning (ML) for distinguishing between printing Impact Statement This study investigates the use of ML for predicting the printing quality given the printing conditions in extrusion-based 3D printing of biomaterials. Classification and regression methods built upon Random Forests show promise for the development of a recommendation system for identifying suitable printing conditions reducing the amount of required experimentation. This study also gives insights on developing an efficient strategy for collecting data for training ML models for predicting printing quality in extrusion-based 3D printing of biomaterials.

JCO Clinical Cancer Informatics, 2020

PURPOSE HLA protein receptors play a key role in cellular immunity. They bind intracellular pepti... more PURPOSE HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures. METHODS Here we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other common...

Scientific Reports

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering i... more Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta’s ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protoc...

Briefings in Bioinformatics, Jul 7, 2023

is a postdoctoral researcher in the Department of Biology and Biochemistry at the University of H... more is a postdoctoral researcher in the Department of Biology and Biochemistry at the University of Houston. His research interests are cancer research, immunotherapy and tumor antigen screening. Hussain Kalavadwala is a PhD student in the Department of Biology and Biochemistry at the University of Houston. His research interests include cell biology, molecular biology and bioinformatics. Martiela V. de Freitas is a postdoctoral researcher in the Department of Biology and Biochemistry at the University of Houston. Her research interests include bioinformatics, genomics, immunology and immunoinformatics. Cecilia Clementi is a professor of computational physics at the Free University of Berlin. She specializes in coarse-grain and multi-scale modelling of biophysical systems to better understand cellular functions. Geancarlo Zanatta is a professor in the Department of Biophysics at Federal University of Rio Grande do Sul (UFRGS). His research focuses on using experimental and computational structural biology to understand pharmacological molecular interactions.

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure de... more Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conform...

Frontiers in Immunology

The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has ... more The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has affected millions of people worldwide. There is constant search for new therapies to either prevent or mitigate the disease. Fortunately, we have observed the successful development of multiple vaccines. Most of them are focused on one viral envelope protein, the spike protein. However, such focused approaches may contribute for the rise of new variants, fueled by the constant selection pressure on envelope proteins, and the widespread dispersion of coronaviruses in nature. Therefore, it is important to examine other proteins, preferentially those that are less susceptible to selection pressure, such as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral response, peptides from its conserved regions can be presented by class I Human Leukocyte Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the increased number of protein ...

Tissue Engineering Part A, 2020

Various material compositions have been successfully used in 3D printing with promising applicati... more Various material compositions have been successfully used in 3D printing with promising applications as scaffolds in tissue engineering. However, identifying suitable printing conditions for new materials requires extensive experimentation in a time and resource-demanding process. This study investigates the use of Machine Learning (ML) for distinguishing between printing Impact Statement This study investigates the use of ML for predicting the printing quality given the printing conditions in extrusion-based 3D printing of biomaterials. Classification and regression methods built upon Random Forests show promise for the development of a recommendation system for identifying suitable printing conditions reducing the amount of required experimentation. This study also gives insights on developing an efficient strategy for collecting data for training ML models for predicting printing quality in extrusion-based 3D printing of biomaterials.

JCO Clinical Cancer Informatics, 2020

PURPOSE HLA protein receptors play a key role in cellular immunity. They bind intracellular pepti... more PURPOSE HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures. METHODS Here we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other common...

Scientific Reports

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering i... more Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta’s ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protoc...