scholarly journals CPdock: The Complementarity Plot for Docking of Proteins: Implementing Multi-dielectric Continuum Electrostatics

2017 ◽  
Author(s):  
Sankar Basu
Keyword(s):  
State Of The Art ◽  
Protein Complexes ◽  
Protein Structures ◽  
Continuum Electrostatics ◽  
Scoring Functions ◽  
Knowledge Based ◽  
Dielectric Continuum ◽  
Validation Tool ◽  
Current Report ◽  
Docking Benchmark

AbstractThe Complementarity plot (CP) is an established validation tool for protein structures, applicable to both, globular proteins (folding) as well as protein-protein complexes (binding). It computes the shape and electrostatic complementarities (Sm, Em) for amino acid side-chains buried within the protein interior or interface and plots them in a two-dimensional plot having knowledge-based probabilistic quality estimates for the residues as well as for the whole structure. The current report essentially presents an upgraded version of the plot with the implementation of the advanced multi-dielectric functionality (as in Delphi version 6.2 or higher) in the computation of electrostatic complementarity to make the validation tool physico-chemically more realistic. The two methods (single‐ and multi-dielectric) agrees decently in their resultant Em values and hence, provisions for both methods have been kept in the software suite. So to speak, the global electrostatic balance within a well-folded protein and / or a well-packed interface seems only marginally perturbed by the choice of different internal dielectric values. However, both from theoretical as well as practical grounds, the more advanced multi-dielectric version of the plot is certainly recommended for potentially producing more reliable results. The report also presents a new methodology and a variant plot, namely, CPdock, based on the same principles of complementarity, specifically designed to be used in the docking of proteins. The efficacy of the method to discriminate between good and bad docked protein complexes have been tested on a recent state-of-the-art docking benchmark. The results unambiguously indicate that CPdock can indeed be effective in the initial screening phase of a docking scoring pipeline before going into more sophisticated and computationally expensive scoring functions. CPdock has been made available at https://github.com/nemo8130/CPdock

scholarly journals ISLAND: in-silico proteins binding affinity prediction using sequence information

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Wajid Arshad Abbasi ◽  
Adiba Yaseen ◽  
Fahad Ul Hassan ◽  
Saiqa Andleeb ◽  
Fayyaz Ul Amir Afsar Minhas
Keyword(s):  
Machine Learning ◽  
Protein Binding ◽  
Binding Affinity ◽  
State Of The Art ◽  
Protein Complexes ◽  
Protein Structures ◽  
Sequence Information ◽  
Binding Affinity Prediction ◽  
Generalization Performance ◽  
Affinity Prediction

Abstract Background Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. Method We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. Results We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. Conclusion This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

scholarly journals Dockground scoring benchmarks for protein docking

2021 ◽  
Author(s):  
Ian Kotthoff ◽  
Petras J. Kundrotas ◽  
Ilya A. Vakser
Keyword(s):  
Protein Complexes ◽  
Protein Docking ◽  
Structural Quality ◽  
Scoring Functions ◽  
Model Benchmark ◽  
User Friendly ◽  
Development And Validation ◽  
Protein Models ◽  
Docking Benchmark

AbstractProtein docking protocols typically involve global docking scan, followed by re-ranking of the scan predictions by more accurate scoring functions that are either computationally too expensive or algorithmically impossible to include in the global scan. Development and validation of scoring methodologies are often performed on scoring benchmark sets (docking decoys) which offer concise and nonredundant representation of the global docking scan output for a large and diverse set of protein-protein complexes. Two such protein-protein scoring benchmarks were built for the Dockground resource, which contains various datasets for the development and testing of protein docking methodologies. One set was generated based on the Dockground unbound docking benchmark 4, and the other based on protein models from the Dockground model-model benchmark 2. The docking decoys were designed to reflect the reality of the real-case docking applications (e.g., correct docking predictions defined as near-native rather than native structures), and to minimize applicability of approaches not directly related to the development of scoring functions (reducing clustering of predictions in the binding funnel and disparity in structural quality of the near-native and non-native matches). The sets were further characterized by the source organism and the function of the protein-protein complexes. The sets, freely available to the research community on the Dockground webpage, present a unique, user-friendly resource for the developing and testing of protein-protein scoring approaches.

Survey of the State-of-the-Art Expert/Knowledge Based Systems in Civil Engineering.

1986 ◽  
Author(s):  
Simon S. Kim ◽  
Mary Lou Maher ◽  
Raymond E. Levitt ◽  
Martin F. Rooney ◽  
Thomas J. Siller
Keyword(s):  
Civil Engineering ◽  
Expert Knowledge ◽  
State Of The Art ◽  
The State ◽  
Knowledge Based Systems ◽  
Knowledge Based

scholarly journals An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein–monosaccharide complexes

Glycobiology ◽  
2018 ◽  
Vol 29 (2) ◽  
pp. 124-136 ◽  
Author(s):  
Juan I Blanco Capurro ◽  
Matias Di Paola ◽  
Marcelo Daniel Gamarra ◽  
Marcelo A Martí ◽  
Carlos P Modenutti
Keyword(s):  
Molecular Dynamics ◽  
Model Building ◽  
Essential Element ◽  
Homology Model ◽  
Conformational Ensemble ◽  
Scoring Functions ◽  
Knowledge Based ◽  
Hydrophilic Nature ◽  
Conformational Diversity ◽  
Biological Recognition

Abstract Unraveling the structure of lectin–carbohydrate complexes is vital for understanding key biological recognition processes and development of glycomimetic drugs. Molecular Docking application to predict them is challenging due to their low affinity, hydrophilic nature and ligand conformational diversity. In the last decade several strategies, such as the inclusion of glycan conformation specific scoring functions or our developed solvent-site biased method, have improved carbohydrate docking performance but significant challenges remain, in particular, those related to receptor conformational diversity. In the present work we have analyzed conventional and solvent-site biased autodock4 performance concerning receptor conformational diversity as derived from different crystal structures (apo and holo), Molecular Dynamics snapshots and Homology-based models, for 14 different lectin–monosaccharide complexes. Our results show that both conventional and biased docking yield accurate lectin–monosaccharide complexes, starting from either apo or homology-based structures, even when only moderate (45%) sequence identity templates are available. An essential element for success is a proper combination of a middle-sized (10–100 structures) conformational ensemble, derived either from Molecular dynamics or multiple homology model building. Consistent with our previous works, results show that solvent-site biased methods improve overall performance, but that results are still highly system dependent. Finally, our results also show that docking can select the correct receptor structure within the ensemble, underscoring the relevance of joint evaluation of both ligand pose and receptor conformation.

scholarly journals Computational design of symmetrical eight-bladed β-propeller proteins

IUCrJ ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Hiroki Noguchi ◽  
Christine Addy ◽  
David Simoncini ◽  
Staf Wouters ◽  
Bram Mylemans ◽  
...  
Keyword(s):  
Enzymatic Activity ◽  
Evolutionary History ◽  
Protein Complexes ◽  
Protein A ◽  
Protein Structures ◽  
Computational Design ◽  
Computational Approach ◽  
Modular Architecture ◽  
Cellular Processes ◽  
History Of

β-Propeller proteins form one of the largest families of protein structures, with a pseudo-symmetrical fold made up of subdomains called blades. They are not only abundant but are also involved in a wide variety of cellular processes, often by acting as a platform for the assembly of protein complexes. WD40 proteins are a subfamily of propeller proteins with no intrinsic enzymatic activity, but their stable, modular architecture and versatile surface have allowed evolution to adapt them to many vital roles. By computationally reverse-engineering the duplication, fusion and diversification events in the evolutionary history of a WD40 protein, a perfectly symmetrical homologue called Tako8 was made. If two or four blades of Tako8 are expressed as single polypeptides, they do not self-assemble to complete the eight-bladed architecture, which may be owing to the closely spaced negative charges inside the ring. A different computational approach was employed to redesign Tako8 to create Ika8, a fourfold-symmetrical protein in which neighbouring blades carry compensating charges. Ika2 and Ika4, carrying two or four blades per subunit, respectively, were found to assemble spontaneously into a complete eight-bladed ring in solution. These artificial eight-bladed rings may find applications in bionanotechnology and as models to study the folding and evolution of WD40 proteins.

scholarly journals Conformational variability in proteins bound to single-stranded DNA: a new benchmark for new docking perspectives

2021 ◽  
Author(s):  
Dominique MIAS-LUCQUIN ◽  
Isaure Chauvot de Beauchêne
Keyword(s):  
Protein Data Bank ◽  
Conformational Changes ◽  
Molecular Interactions ◽  
Protein Structures ◽  
Data Bank ◽  
Computational Docking ◽  
Ssdna Binding ◽  
Conformational Variability ◽  
High Flexibility ◽  
Docking Benchmark

We explored the Protein Data-Bank (PDB) to collect protein-ssDNA structures and create a multi-conformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included. For the 143 groups identified as bound-unbound structures of the same protein , we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions, and compared it to the supposedly binding-induced modifications. This benchmark is, to our knowledge, the first attempt made to peruse available structures of protein – ssDNA interactions to such an extent, aiming to improve computational docking tools dedicated to this kind of molecular interactions.

scholarly journals PB-kPRED: knowledge-based prediction of protein backbone conformation using a structural alphabet

2017 ◽  
Author(s):  
Iyanar Vetrivel ◽  
Swapnil Mahajan ◽  
Manoj Tyagi ◽  
Lionel Hoffmann ◽  
Yves-Henri Sanejouand ◽  
...  
Keyword(s):  
Protein Structures ◽  
Scoring Function ◽  
Evolutionary Information ◽  
Structural Alphabet ◽  
Local Structures ◽  
Knowledge Based ◽  
Scanning Strategies ◽  
Accuracy Of Prediction ◽  
The Impact ◽  
Protein Blocks

AbstractLibraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks (PBs), is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of PBs. Thus, predicting the local structure of a protein in terms of protein blocks is a step towards the objective of predicting its 3-D structure. Here a new approach, kPred, is proposed towards this aim that is independent of the evolutionary information available. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) apply a purely knowledge-based algorithm, not relying on secondary structure predictions or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures.Based on the strategy used for scanning the database, the method was able to achieve efficient mean Q16 accuracies between 40.8% and 66.3% for a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. The impact of these scanning strategies on the prediction was evaluated and is discussed. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

scholarly journals Simplified geometric representations of protein structures identify complementary interaction interfaces

2019 ◽  
Author(s):  
Caitlyn L. McCafferty ◽  
Edward M. Marcotte ◽  
David W. Taylor
Keyword(s):  
Conformational Changes ◽  
Protein Interaction ◽  
Protein Function ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Protein Complexes ◽  
Protein Structures ◽  
Molecular Properties ◽  
3D Structures ◽  
Geometric Representations

ABSTRACTProtein-protein interactions are critical to protein function, but three-dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large-scale prediction and assembly of 3D structures of multi-protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a cuboid transformation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and mutation rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner-specific potential surface complementarity. On two available benchmarks of 85 overall known protein complexes, we observed F1 scores (a weighted combination of precision and recall) of 19-34% at correctly identifying protein interaction surfaces, comparable to more computationally intensive 3D docking methods in the annual Critical Assessment of PRedicted Interactions. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor-ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å RMSD. Thus, a cuboid transformation of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.

Physics-based predictions of RNA loop stability and structures

2012 ◽  
Author(s):  
◽  
Liang Liu
Keyword(s):  
Free Energy ◽  
Building Blocks ◽  
Conformational Ensemble ◽  
Scoring Functions ◽  
Rna Structures ◽  
Cellular Functions ◽  
Entropy Model ◽  
University Of Missouri ◽  
Knowledge Based ◽  
Three Body

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] RNA (ribonucleic acid) molecules play a variety of crucial roles in cellular functions at the level of transcription, translation and gene regulation. RNA functions are tied to structures. We aim to develop a novel free energy-based model for RNA structures, especially for RNA loops and junctions. In the first project, we develop a new conformational entropy model for RNA structures consisting of multiple helices connected by cross-linked loops. The basic strategy of our approach is to decompose the whole structure into a number of three-body building blocks, where each building block consists of a loop and two helices that are directly connected to the two ends of the loop. Assembly of the building blocks gives the entropy of the whole structure. The method provide a solid first step toward a systematic development of an entropy and free energy model for complex tertiary folds for RNA and other biopolymer. In the second project, based on the survey of all the known RNA structures, we derive a set of virtual bond-based scoring functions for the different types of dinucleotides. To circumvent the problem of reference state selection, we apply an iterative method to extract the effective potential, based on the complete conformational ensemble. With such a set of knowledge-based energy parameters, for a given sequence, we can successfully identify the native structure (the best-scored structure) from a set of structural decoys.

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