pwc-archive/papers-with-abstracts huggingface dataset:
Monte Carlo for Protein Structures
Authors: Juami H. M. van Gils, Maurits Dijkstra, Halima Mouhib, Arriën Symon Rauh, Jocelyne Vreede, K. Anton Feenstra, Sanne Abeln
Date: 2023-07-05 00:00:00
Tasks: Protein Structure 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. 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.
Molecular Dynamics
Authors: Halima Mouhib, Juami H. M. van Gils, Jose Gavaldá-Garciá, Qingzhen Hou, Ali May, Arriën Symon Rauh, Jocelyne Vreede, Sanne Abeln, K. Anton Feenstra
Date: 2023-07-05 00:00:00
Tasks: Protein Structure 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. We know that many proteins have functional motions, and in Chapter "Structure Determination" we already introduced the famous example of the allosteric cooperative binding of oxygen to the haem group in hemoglobin. However, experimentally, such motions are hard to observe. Here, we will introduce MD simulations to investigate the dynamic behaviour of proteins. In a simulation the forces and interactions between particles are used to numerically derive the resulting three-dimensional movement of these particles over a certain time-scale. We will also highlight some applications, and will see how simulation results may be interpreted.
Thermodynamics of Protein Folding
Authors: Juami H. M. van Gils, Halima Mouhib, Erik van Dijk, Maurits Dijkstra, Isabel Houtkamp, Arthur Goetzee, Sanne Abeln, K. Anton Feenstra
Date: 2023-07-05 00:00:00
Tasks: Protein Folding, Protein Structure 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. 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.
Introduction to Protein Folding
Authors: Juami H. M. van Gils, Erik van Dijk, Ali May, Halima Mouhib, Jochem Bijlard, Annika Jacobsen, Isabel Houtkamp, K. Anton Feenstra, Sanne Abeln
Date: 2023-07-05 00:00:00
Tasks: Protein Folding, Protein Structure 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. In this chapter we explore basic physical and chemical concepts required to understand protein folding. We introduce major (de)stabilising factors of folded protein structures such as the hydrophobic effect and backbone entropy. In addition, we consider different states along the folding pathway, as well as natively disordered proteins and aggregated protein states. In this chapter, an intuitive understanding is provided about the protein folding process, to prepare for the next chapter on the thermodynamics of protein folding. In particular, it is emphasized that protein folding is a stochastic process and that proteins unfold and refold in a dynamic equilibrium. The effect of temperature on the stability of the folded and unfolded states is also explained.
Function Prediction
Authors: Bas Stringer, Annika Jacobsen, Qingzhen Hou, Hans de Ferrante, Olga Ivanova, Katharina Waury, Jose Gavaldá-Garciá, Sanne Abeln, K. Anton Feenstra
Date: 2023-07-05 00:00:00
Tasks: Prediction, Protein Structure 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. There are still huge gaps in understanding the molecular function of proteins. This raises the question on how we may predict protein function, when little to no knowledge from direct experiments is available. Protein function is a broad concept which spans different scales: from quantum scale effects for catalyzing enzymatic reactions, to phenotypes that manifest at the organism level. In fact, many of these functional scales are entirely different research areas. Here, we will consider prediction of a smaller range of functions, roughly spanning the protein residue-level up to the pathway level. We will give a conceptual overview of which functional aspects of proteins we can predict, which methods are currently available, and how well they work in practice.
Structural Property Prediction
Authors: Maurits Dijkstra, Punto Bawono, Isabel Houtkamp, Jose Gavaldá-Garciá, Mascha Okounev, Robbin Bouwmeester, Bas Stringer, Jaap Heringa, Sanne Abeln, K. Anton Feenstra, Juami H. M. van Gils
Date: 2023-07-05 00:00:00
Tasks: Benchmarking, Prediction, Property Prediction, Protein Structure 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.
Data Resources for Structural Bioinformatics
Authors: Jose Gavaldá-Garciá, Bas Stringer, Olga Ivanova, Sanne Abeln, K. Anton Feenstra, Halima Mouhib
Date: 2023-07-05 00:00:00
Tasks: Protein Structure 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. Structural bioinformatics involves a variety of computational methods, all of which require input data. Typical inputs include protein structures and sequences, which are usually retrieved from a public or private database. This chapter introduces several key resources that make such data available, as well as a handful of tools that derive additional information from experimentally determined or computationally predicted protein structures and sequences.
Structure Alignment
Authors: Olga Ivanova, Jose Gavaldá-Garciá, Dea Gogishvili, Isabel Houtkamp, Robbin Bouwmeester, K. Anton Feenstra, Sanne Abeln
Date: 2023-07-05 00:00:00
Tasks: Protein Structure 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. The Protein DataBank (PDB) contains a wealth of structural information. In order to investigate the similarity between different proteins in this database, one can compare the primary sequence through pairwise alignment and calculate the sequence identity (or similarity) over the two sequences. This strategy will work particularly well if the proteins you want to compare are close homologs. However, in this chapter we will explain that a structural comparison through structural alignment will give you much more valuable information, that allows you to investigate similarities between proteins that cannot be discovered by comparing the sequences alone.
Introduction to Protein Structure
Authors: Annika Jacobsen, Erik van Dijk, Halima Mouhib, Bas Stringer, Olga Ivanova, Jose Gavaldá-Garciá, Laura Hoekstra, K. Anton Feenstra, Sanne Abeln
Date: 2023-07-05 00:00:00
Tasks: Protein Folding, Protein Structure 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. Within the living cell, protein molecules perform specific functions, typically by interacting with other proteins, DNA, RNA or small molecules. They take on a specific three dimensional structure, encoded by its amino acid sequence, which allows them to function within the cell. Hence, the understanding of a protein's function is tightly coupled to its sequence and its three dimensional structure. Before going into protein structure analysis and prediction, and protein folding and dynamics, here, we give a short and concise introduction into the basics of protein structures.
In Silico Tools in PROTACs design
Authors: Mengman Wei
Date: 2023-07-03 00:00:00

PROTACs, as a highly promising new. therapeutic paradigm, have attracted widespread attention from the academic and pharmaceutical communities in recent years. To date, the design and validation of PROTACs molecule's druggability primarily rely on experimental approaches, making the development process of this kind of drug molecule time-consuming. Computer-aided tools for PROTACs design may offer a potential solution to expedite the design process and enhance its efficiency. This mini review briefly summarizes the in silico tools for PROTACs drug molecule design reported recently.
Mechanical compression regulates tumor spheroid invasion into a 3D collagen matrix
Authors: Mrinal Pandey, Young Joon Suh, Minha Kim, Hannah Jane Davis, Jeffrey E Segall, Mingming Wu
Date: 2023-07-03 00:00:00

Uncontrolled growth of tumor cells in confined spaces leads to the accumulation of compressive stress within the tumor. Although the effects of tension within 3D extracellular matrices on tumor growth and invasion are well established, the role of compression in tumor mechanics and invasion is largely unexplored. In this study, we modified a Transwell assay such that it provides constant compressive loads to spheroids embedded within a collagen matrix. We used microscopic imaging to follow the single cell dynamics of the cells within the spheroids, as well as invasion into the 3D extracellular matrices (EMCs). Our experimental results showed that malignant breast tumor (MDA-MB-231) and non-tumorigenic epithelial (MCF10A) spheroids responded differently to a constant compression. Cells within the malignant spheroids became more motile within the spheroids and invaded more into the ECM under compression; whereas cells within non-tumorigenic MCF10A spheroids became less motile within the spheroids and did not display apparent detachment from the spheroids under compression. These findings suggest that compression may play differential roles in healthy and pathogenic epithelial tissues and highlights the importance of tumor mechanics and invasion.
Grammatical Parameters from a Gene-like Code to Self-Organizing Attractors
Authors: Giuseppe Longobardi, Alessandro Treves
Date: 2023-07-06 00:00:00
Tasks: Diversity

Parametric approaches to grammatical diversity range from Chomsky's 1981 classical Principles & Parameters model to minimalist reinterpretations: in some proposals of the latter framework, parameters need not be an extensional list given at the initial state S0 of the mind, but can be constructed through a bio-program in the course of language development. In this contribution we pursue this lead and discuss initial data and ideas relevant for the elaboration of three sets of questions: 1) how can binary parameters be conceivably implemented in cortical and subcortical circuitry in the human brain? 2) how can parameter mutations be taken to occur? 3) given the distribution of parameter values across languages and their implications, can multi-parental models of language phylogenies, departing from ultrametricity, also account for some of the available evidence?
Spontaneous segregation of visual information between parallel streams of a multi-stream convolutional neural network
Authors: Hiroshi Tamura
Date: 2023-07-06 00:00:00

Visual information is processed in hierarchically organized parallel pathways in the primate brain. In lower cortical areas, color information and shape information are processed in a parallel manner, while in higher cortical areas, various types of visual information, such as color, face, animate/inanimate, are processed in a parallel manner. In the present study, the possibility of spontaneous segregation of visual information in parallel streams was examined by constructing a convolutional neural network with parallel architecture in all of the convolutional layers. The results revealed that color information was segregated from shape information in most model instances. Deletion of the color-related stream decreased recognition accuracy in the inanimate category, whereas deletion of the shape-related stream decreased recognition accuracy in the animate category. The results suggest that properties of filters and functions of a stream are spontaneously segregated in parallel streams of neural networks.
Modelling Spontaneous Firing Activity of the Motor Cortex in a Spiking Neural Network with Random and Local Connectivity
Authors: Lysea Haggie, Thor Besier, Angus McMorland
Date: 2023-07-05 00:00:00

Computational models of cortical activity provide insight into the mechanisms of higher-order processing in the human brain including planning, perception and the control of movement. Activity in the cortex is ongoing even in the absence of sensory input or discernible movements and is thought to be linked to the topology of cortical circuitry. However, the connectivity and its functional role in the generation of spatio-temporal firing patterns and cortical computations are still unknown. Movement of the body is a key function of the brain, with the motor cortex the main cortical area implicated in the generation of movement. We built a spiking neural network model of the motor cortex which incorporates a laminar structure and circuitry based on a previous cortical model. A local connectivity scheme was implemented to introduce more physiological plausibility to the cortex model, and the effect on the rates, distributions and irregularity of neuronal firing was compared to the original random connectivity method and experimental data. Local connectivity increased the distribution of and overall rate of neuronal firing. It also resulted in the irregularity of firing being more similar to those observed in experimental measurements. The larger variability in dynamical behaviour of the local connectivity model suggests that the topological structure of the connections in neuronal population plays a significant role in firing patterns during spontaneous activity. This model took steps towards replicating the macroscopic network of the motor cortex, replicating realistic spatiotemporal firing to shed light on information coding in the cortex. Large scale computational models such as this one can capture how structure and function relate to observable neuronal firing behaviour, and investigates the underlying computational mechanisms of the brain.
Comparing dendritic trees with actual trees
Authors: Roozbeh Farhoodi, Phil Wilkes, Anirudh M. Natarajan, Samantha Ing-Esteves, Julie L. Lefebvre, Mathias Disney, Konrad P. Kording
Date: 2023-07-04 00:00:00

Since they became observable, neuron morphologies have been informally compared with biological trees but they are studied by distinct communities, neuroscientists, and ecologists. The apparent structural similarity suggests there may be common quantitative rules and constraints. However, there are also reasons to believe they should be different. For example, while the environments of trees may be relatively simple, neurons are constructed by a complex iterative program where synapses are made and pruned. This complexity may make neurons less self-similar than trees. Here we test this hypothesis by comparing the features of segmented sub-trees with those of the whole tree. We indeed find more self-similarity within actual trees than neurons. At the same time, we find that many other features are somewhat comparable across the two. Investigation of shapes and behaviors promises new ways of conceptualizing the form-function link.