- Ulrich H. E. Hansmann, Adam Liwo and Cezary Czaplewski Foreword | full text
- Yi He, H. A. Scheraga and S. Rackovsky A New Approach to Homology Modeling abstract | full text
- Andrzej Kolinski, Sebastian Kmiecik, Michal Jamroz, Maciej Błaszczyk, Maksim Kouza and Mateusz Kurcinski Coarse-Grained Modeling of Protein Structure, Dynamics and Protein-Protein Interactions abstract | full text
- Sumudu P. Leelananda, Marcin Pawlowski, Eshel Faraggi and Andrzej Kloczkowski Improving Protein Structure Prediction, Refinement and Quality Assessment Techniques abstract | full text
- Nguyen Truong Co, Man Hoang Viet, Phan Minh Truong, Maksim Kouza and Mai Suan Li Key Factors Governing Fibril Formation of Proteins: Insights from Simulations and Experiments abstract | full text
- Ewa Broclawik, Tomasz Borowski and Mariusz Radoń Spin and Electron Density Redistribution upon Binding of Non-Innocent Ligand by Iron in Enzymatic Environment: Challenges for Quantum Chemistry abstract | full text
- Pawel Dabrowski-Tumanski, Szymon Niewieczerzal and Joanna I. Sulkowska Determining Critical Amino Acid Contacts for Knotted Protein Folding abstract | full text
- Ruihan Zhang, Jochen Erler and J\"org Langowski Molecular Dynamics Simulation of Histone H3 and H4 N-Terminal Tail Conformation in the Presence and Absence of Nucleosome Core abstract | full text
- Rafal Jakubowski, Anna Gogolinska, Lukasz Peplowski, Piotr Skrzyniarz and Wieslaw Nowak Computational Studies of TTR Related Amyloidosis: Exploration of Conformational Space through Petri Net-Based Algorithm abstract | full text
hYi He, H. A. Scheraga and S. Rackovsky A New Approach to Homology Modeling
The need to interpret experimental results led to, first, an all-atom force field, followed by a coarse-grained one. As an aid to these force fields, a new approach is introduced here to predict protein structure based on the physical properties of the amino acids. This approach includes three key components: Kidera factors describing the physical properties, Fourier transformation and UNRES coarse-grained force field simulations. Different from traditional homology modeling methods which are based on evolution, this approach is physics-based, and does not have the same weaknesses as the traditional homology modeling methods. Our results show that this approach can produce above average prediction results, and can be used as a useful tool for protein structure prediction.
hAndrzej Kolinski, Sebastian Kmiecik, Michal Jamroz, Maciej Błaszczyk, Maksim Kouza and Mateusz Kurcinski Coarse-Grained Modeling of Protein Structure, Dynamics and Protein-Protein Interactions
Theoretical prediction of protein structures and dynamics is essential for understanding the molecular basis of drug action, metabolic and signaling pathways in living cells, designing new technologies in the life science and material sciences. We developed and validated a novel multiscale methodology for the study of protein folding processes including flexible docking of proteins and peptides. The new modeling technique starts from coarse-grained large-scale simulations, followed by selection of the most plausible final structures and intermediates and, finally, by an all-atom rectification of the obtained structures. Except for the most basic bioinformatics tools, the entire computational methodology is based on the models and algorithms developed in our lab. The coarse-grained simulations are based on a high-resolution lattice representation of protein structures, a knowledge based statistical force field and efficient Monte Carlo dynamics schemes, including Replica Exchange algorithms. This paper focuses on the description of the coarse-grained CABS model and its selected applications.
hSumudu P. Leelananda, Marcin Pawlowski, Eshel Faraggi and Andrzej Kloczkowski Improving Protein Structure Prediction, Refinement and Quality Assessment Techniques
Several novel techniques have been combined to improve protein structure prediction, structural refinement and quality assessment of protein models. We discuss in brief the development of four-body potentials that take into account dense packing and cooperativity of interactions of proteins, and its success. We have developed a method that uses whole protein information filtered through machine learning to score protein models based on their likeness to native structure. Here we consider electrostatic interactions and residue depth, and use these for structure prediction. These potentials were tested to be successful in CASP9 and CASP10. We have also developed a Quality Assessment technique, MQAPsingle, which is a quasi-single-model MQAP, by combining advantages of both “pure” single-model MQAPs and clustering MQAPs. This technique can be used in ranking and assessing the absolute global quality of single protein models. This model (Pawlowski-Kloczkowski) was ranked 3rd in Model Quality Assessment in CASP10. Consideration of protein flexibility and its fluctuation dynamics improves protein structure prediction and leads to better refinement of computational models of proteins. Here we also discuss how Anisotropic Network Model (ANM) of protein fluctuation dynamics and Go-like model of energy score can be used for novel protein structure refinement.
hNguyen Truong Co, Man Hoang Viet, Phan Minh Truong, Maksim Kouza and Mai Suan Li Key Factors Governing Fibril Formation of Proteins: Insights from Simulations and Experiments
Fibril formation of proteins and peptides is associated with a large group of major human diseases, including Alzheimer's disease, prion disorders, amyotrophic lateral sclerosis, type 2 diabetes, etc. Therefore, understanding the key factors that govern this process is of paramount importance. The fibrillogenesis of polypeptide chains depends on their intrinsic properties as well as on the external conditions. In this mini-review we discuss the relationship between fibril formation kinetics and the sequence, aromaticity, hydrophobicity, charge and population of the so called fibril-prone conformation in a monomer state. The higher the population, the faster is the fibril elongation and this dependence may be described by a single exponential function. This observation opens up a new way to understand the fibrillogenesis of bio-molecules at the monomer level. We will also discuss the influence of the environment with focus on the recently observed dual effect of crowders on the aggregation rates of polypeptide chains.
hEwa Broclawik, Tomasz Borowski and Mariusz Radoń Spin and Electron Density Redistribution upon Binding of Non-Innocent Ligand by Iron in Enzymatic Environment: Challenges for Quantum Chemistry
The quality of the description of a chemical bond between the metal (active site) and the ligand (substrate) critically depends on the electronic processes accompanying the bond formation. However, as far as transition metal centers (TM) in enzymes are considered, most of the properties related to their electronic structure are extremely challenging for quantum chemistry. Especially severe problems appear for the bonding of NO to ferrous sites, e.g. in myoglobin or non-heme enzymes. Therefore, special care has to be shown in the assessment of a quantum chemical method employed with respect to its power in describing the properties of interest. In this work we discuss spin-resolved Fe-NO charge transfers and their relation to the metal spin state, with special attention paid to the interpretation of the bonding between NO and the transition metal center in terms of dative or covalent contributions; furthermore, the impact of spin and the electron transfer on the reactivity of the center is discussed. The stress is put on the role of the coordinating environment in controlling the reaction mechanism via fine-tuning of the spin and the oxidation status of the metal core. This goes in line with the high significance of spin in enzymatic reaction mechanisms (cf. multi-state reactivity proposed for iron enzymes).
hPawel Dabrowski-Tumanski, Szymon Niewieczerzal and Joanna I. Sulkowska Determining Critical Amino Acid Contacts for Knotted Protein Folding
Proteins with a non-trivial topological structure are currently well recognized, while a knotted protein chain represents a new motif in protein three dimensional folds. Recent comprehensive analysis of the Protein Data Base shows that knotted proteins represent 1.5% of known protein structures. Determination of a free energy landscape of knotted proteins, and its understanding provides a serious challenge for both experimentalists and theoreticians. Moreover the role of a knot for biological activity of protein still remains elusive. In this work we study the smallest knotted proteins (PDB code 2efv) to understand/investigate their free energy landscape, by means of extensive molecular dynamics simulations. We explore the dependence of the thermodynamics, kinetics and protein folding pathways on the native-likes contact maps and on the length of the chain. We analyze two sets of native-like contacts, which differ by a number of long range interactions, and we consider the 2efv protein with two different lengths of its C-terminus end. We identify the subset of native contacts sufficient to explore the entire free energy landscape. Then, we analyze the influence of the remaining set of native contacts – we show that the set of additional contacts may enhance folding kinetics, and that it has an influence on folding pathways.
hRuihan Zhang, Jochen Erler and J\"org Langowski Molecular Dynamics Simulation of Histone H3 and H4 N-Terminal Tail Conformation in the Presence and Absence of Nucleosome Core
Histone N(C)-terminal tails play an important role in chromatin remodeling and gene regulation. Posttranslational modifications (PTMs) of histone tails are known to be correlated to distinct states of gene activity, but the molecular mechanism of these processes is largely unknown. PTMs alter the electrostatic environment and conformation of histone tails, as well as their interaction with other components. Here we performed extensive Replica Exchange Molecular Dynamics (REMD) simulations for the H4 and H3 tails, isolated and with inclusion of a nucleosome. Our results agree with the predictions of previous theoretical studies for the secondary structure of isolated tails, but show strong dependence on the force field used. In the presence of the nucleosome, the secondary structure of histone tails is destabilized. Furthermore, H4K16 is found to insert into the DNA minor groove, whereas the acetylated H4K16 stays on the surface of DNA.
hRafal Jakubowski, Anna Gogolinska, Lukasz Peplowski, Piotr Skrzyniarz and Wieslaw Nowak Computational Studies of TTR Related Amyloidosis: Exploration of Conformational Space through Petri Net-Based Algorithm
Amyloidosis, a serious and widespread disease with a genetic background, manifests itself through the formation of dangerous fibrils in various organs. Apart from the polluted environment and an unhealthy lifestyle, genetic factors may accelerate this process leading in some cases to lethal damages to the body. Recently, a growing interest in amyloidogenic protein research has been observed. Transthyretin (TTR) is a tetrameric protein that transports thyroid hormone thyroxine and retinol binding protein in plasma and the cerebral ﬂuid. Sometimes TTR breaks apart and forms fibrils. Several single point mutations, having destabilizing impact on the TTR complex, are involved in the amyloidogenic TTR cascade. Problems with the TTR tetramer stability and conformational space characteristics of the protein have not been addressed computationally before. We present selected results of our molecular dynamics (MD, ∼ 2 000 ns) and steered MD simulations (SMD) of three variants of TTR: Wild Type (WT), V30M and L55P. SMD has been used to enforce the dissociation of TTR. Conformational spaces of WT TTR and its amyloidogenic variants have been investigated using a novel “One Place One Conformation” (OPOC) algorithm based on a graph technique called Petri net (PN) formalism. While the PN approach alone does not permit a direct identification of protein regions with reduced stability, it gives quite a useful tool for an effective comparison of complex protein energy landscapes explored during classical andór SMD steered molecular dynamics simulations.