56 research outputs found
A new curriculum for ethology & student skills in the Netherlands: Part 2: Innovation & implementation activities
Monte Carlo for Protein Structures
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
In the previous chapter "Molecular Dynamics" we have considered protein
simulations from a dynamical point of view, using Newton's laws. In the current
Chapter, we first take a step back and return to the bare minimum needed to
simulate proteins, and show that proteins may be simulated in a more simple
fashion, using the partition function directly. This means we do not have to
calculate explicit forces, velocities, moments and do not even consider time
explicitly. Instead, we will rely on the fact that for most systems we will
want to simulate, the system is in a dynamic equilibrium; and that we want to
find the most stable states in such systems by determining the relative
stabilities between those states.Comment: editorial responsability: Juami H. M. van Gils, K. Anton Feenstra,
Sanne Abeln. This chapter is part of the book "Introduction to Protein
Structural Bioinformatics". The Preface arXiv:1801.09442 contains links to
all the (published) chapters. The update adds available arxiv hyperlinks for
the chapter
Monte Carlo for Protein Structures
While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In the previous chapter "Molecular Dynamics" we have considered protein simulations from a dynamical point of view, using Newton's laws. In the current Chapter, we first take a step back and return to the bare minimum needed to simulate proteins, and show that proteins may be simulated in a more simple fashion, using the partition function directly. This means we do not have to calculate explicit forces, velocities, moments and do not even consider time explicitly. Instead, we will rely on the fact that for most systems we will want to simulate, the system is in a dynamic equilibrium; and that we want to find the most stable states in such systems by determining the relative stabilities between those states
Thermodynamics of Protein Folding
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
In the previous chapter, "Introduction to Protein Folding", we introduced the
concept of free energy and the protein folding landscape. Here, we provide a
deeper, more formal underpinning of free energy in terms of the entropy and
enthalpy; to this end, we will first need to better define the meaning of
equilibrium, entropy and enthalpy. When we understand these concepts, we will
come back for a more quantitative explanation of protein folding and dynamics.
We will discuss the influence of temperature on the free energy landscape, and
the difference between microstates and macrostates.Comment: editorial responsability: Juami H. M. van Gils, K. Anton Feenstra,
Sanne Abeln. This chapter is part of the book "Introduction to Protein
Structural Bioinformatics". The Preface arXiv:1801.09442 contains links to
all the (published) chapters. The update adds available arxiv hyperlinks for
the chapter
Thermodynamics of Protein Folding
While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In the previous chapter, "Introduction to Protein Folding", we introduced the concept of free energy and the protein folding landscape. Here, we provide a deeper, more formal underpinning of free energy in terms of the entropy and enthalpy; to this end, we will first need to better define the meaning of equilibrium, entropy and enthalpy. When we understand these concepts, we will come back for a more quantitative explanation of protein folding and dynamics. We will discuss the influence of temperature on the free energy landscape, and the difference between microstates and macrostates
Structural Property Prediction
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
Some structural properties of proteins that are closely linked to their
function may be easier (or much faster) to predict from sequence than the
complete tertiary structure; for example, secondary structure, surface
accessibility, flexibility, disorder, interface regions or hydrophobic patches.
Serving as building blocks for the native protein fold, these structural
properties also contain important structural and functional information not
apparent from the amino acid sequence. Here, we will first give an introduction
into the application of machine learning for structural property prediction,
and explain the concepts of cross-validation and benchmarking. Next, we will
review various methods that incorporate knowledge of these concepts to predict
those structural properties, such as secondary structure, surface
accessibility, disorder and flexibility, and aggregation.Comment: editorial responsability: Juami H. M. van Gils, K. Anton Feenstra,
Sanne Abeln. This chapter is part of the book "Introduction to Protein
Structural Bioinformatics". The Preface arXiv:1801.09442 contains links to
all the (published) chapter
Explaining disease using big data: How valid is your pathway?
The design of solutions to current societal challenges in human health, healthcare and nutrition, and to the sustainable production of food, feed and energy, requires academic innovations and industrial activity based on life science R&D in its broadest sense. The diversity of on-going programs shows that public-private collaboration is increasing in each of these sectors. A few examples in The Netherlands alone include the Dutch Techcenter for Life Sciences (DTL), CTMM-TraIT (TransMart, Open Clinica), NFU Data 4 Lifesciences initiative, Onco-XL, Parelsnoer, Centre for Personalized Cancer Treatment (CPCT) and Philips' Health-Suite Digital Platform in the Life Science & Health sector; Breed4Food and TIFN in Agri&Food; Virtual Lab for Plant Breeding, Seed Valley and 'Tuinbouw Digitaal' in Horticulture; and BeBasic in Biobased Economy.style the text
ProteinGLUE multi-task benchmark suite for self-supervised protein modeling
Self-supervised language modeling is a rapidly developing approach for the analysis of protein sequence data. However, work in this area is heterogeneous and diverse, making comparison of models and methods difficult. Moreover, models are often evaluated only on one or two downstream tasks, making it unclear whether the models capture generally useful properties. We introduce the ProteinGLUE benchmark for the evaluation of protein representations: a set of seven per-amino-acid tasks for evaluating learned protein representations. We also offer reference code, and we provide two baseline models with hyperparameters specifically trained for these benchmarks. Pre-training was done on two tasks, masked symbol prediction and next sentence prediction. We show that pre-training yields higher performance on a variety of downstream tasks such as secondary structure and protein interaction interface prediction, compared to no pre-training. However, the larger base model does not outperform the smaller medium model. We expect the ProteinGLUE benchmark dataset introduced here, together with the two baseline pre-trained models and their performance evaluations, to be of great value to the field of protein sequence-based property prediction. Availability: code and datasets from https://github.com/ibivu/protein-glue
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