AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes

Abstract The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server’s predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-based local structure biasing terms, the addition of homologous template and co-evolutionary restraints to AWSEM-Suite greatly improves the predictive power of pure AWSEM structure prediction. From the independent evaluation metrics released in the CASP13 experiment, AWSEM-Suite proves to be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free modeling category of CASP13. The AWSEM-Suite server also features a front end with a user-friendly interface. The AWSEM-Suite server is a powerful tool for predicting monomeric protein tertiary structures that is most useful when a suitable structure template is not available. The AWSEM-Suite server is freely available at: https://awsem.rice.edu.

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All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds

BioMed Research International ◽

10.1155/2017/5760612 ◽

2017 ◽

Vol 2017 ◽

pp. 1-17 ◽

Cited By ~ 3

Author(s):

Majid Masso

Keyword(s):

Structure Prediction ◽

Protein Structures ◽

Binding Energies ◽

Coarse Grained ◽

Amber Force Field ◽

Statistical Potentials ◽

Knowledge Based ◽

Native Proteins ◽

Interacting Atoms ◽

Atomic Coordinates

Recent advances in understanding protein folding have benefitted from coarse-grained representations of protein structures. Empirical energy functions derived from these techniques occasionally succeed in distinguishing native structures from their corresponding ensembles of nonnative folds or decoys which display varying degrees of structural dissimilarity to the native proteins. Here we utilized atomic coordinates of single protein chains, comprising a large diverse training set, to develop and evaluate twelve all-atom four-body statistical potentials obtained by exploring alternative values for a pair of inherent parameters. Delaunay tessellation was performed on the atomic coordinates of each protein to objectively identify all quadruplets of interacting atoms, and atomic potentials were generated via statistical analysis of the data and implementation of the inverted Boltzmann principle. Our potentials were evaluated using benchmarking datasets from Decoys-‘R’-Us, and comparisons were made with twelve other physics- and knowledge-based potentials. Ranking 3rd, our best potential tied CHARMM19 and surpassed AMBER force field potentials. We illustrate how a generalized version of our potential can be used to empirically calculate binding energies for target-ligand complexes, using HIV-1 protease-inhibitor complexes for a practical application. The combined results suggest an accurate and efficient atomic four-body statistical potential for protein structure prediction and assessment.

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FOLDING ELASTIC TRANSMEMBRANE HELICES TO FIT IN A LOW-RESOLUTION IMAGE BY ELECTRON MICROSCOPY

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720011005720 ◽

2011 ◽

Vol 09 (supp01) ◽

pp. 37-50 ◽

Cited By ~ 3

Author(s):

YUTAKA UENO ◽

KAZUNORI KAWASAKI ◽

OSAMU SAITO ◽

MASAFUMI ARAI ◽

MAKIKO SUWA

Keyword(s):

Membrane Proteins ◽

Structure Prediction ◽

Molecular Model ◽

Imaging Techniques ◽

Transmembrane Helix ◽

Prediction Method ◽

Molecular Shape ◽

Coarse Grained ◽

Transmembrane Helices ◽

Low Resolution

Structure prediction of membrane proteins could be constrained and thereby improved by introducing data of the observed molecular shape. We studied a coarse-grained molecular model that relied on residue-based dummy atoms to fold the transmembrane helices of a protein in the observed molecular shape. Based on the inter-residue potential, the α-helices were folded to contact each other in a simulated annealing protocol to search optimized conformation. Fitting the model into a three-dimensional volume was tested for proteins with known structures and resulted in a fairly reasonable arrangement of helices. In addition, the constraint to the packing transmembrane helix with the two-dimensional region was tested and found to work as a very similar folding guide. The obtained models nicely represented α-helices with the desired slight bend. Our structure prediction method for membrane proteins well demonstrated reasonable folding results using a low-resolution structural constraint introduced from recent cell-surface imaging techniques.

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APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction

Computational Biology and Chemistry ◽

10.1016/j.compbiolchem.2015.08.006 ◽

2015 ◽

Vol 59 ◽

pp. 142-157 ◽

Cited By ~ 28

Author(s):

Bruno Borguesan ◽

Mariel Barbachan e Silva ◽

Bruno Grisci ◽

Mario Inostroza-Ponta ◽

Márcio Dorn

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Three Dimensional ◽

Knowledge Based

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Swarm-CG: Automatic Parametrization of Bonded Terms in Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

10.26434/chemrxiv.12613427 ◽

2020 ◽

Author(s):

Charly Empereur-mot ◽

Luca Pesce ◽

Davide Bochicchio ◽

Claudio Perego ◽

Giovanni M. Pavan

Keyword(s):

Particle Swarm Optimization ◽

State Of The Art ◽

Structural Complexity ◽

Coarse Grained ◽

Complex Molecules ◽

Swarm Optimization ◽

Molecular Systems ◽

Self Tuning ◽

User Friendly ◽

Python Package

We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms within 4h-24h on standard desktop machines, using an AA trajectory as reference and default settings of the software. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of molecular systems interesting for bio- and nanotechnology. Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for the optimization of models built based also on other CG schemes. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data are available at: www.github.com/GMPavanLab/SwarmCG.

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FALCON2: a web server for high-quality prediction of protein tertiary structures

BMC Bioinformatics ◽

10.1186/s12859-021-04353-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Lupeng Kong ◽

Fusong Ju ◽

Haicang Zhang ◽

Shiwei Sun ◽

Dongbo Bu

Keyword(s):

Protein Structure ◽

Ab Initio ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Web Server ◽

High Quality ◽

Tertiary Structures ◽

Target Proteins ◽

High Quality Protein ◽

Protein Functions

Abstract Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions.

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ONTOLOGY-BASED WEB TOOLS FOR RETRIEVING PHOTOGRAMMETRIC CULTURAL HERITAGE MODELS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w10-31-2019 ◽

2019 ◽

Vol XLII-2/W10 ◽

pp. 31-38 ◽

Cited By ~ 1

Author(s):

M. Ben Ellefi ◽

P. Drap ◽

O. Papini ◽

D. Merad ◽

J. P. Royer ◽

...

Keyword(s):

Cultural Heritage ◽

Specific Knowledge ◽

Domain Specific ◽

Knowledge Based ◽

Web Tools ◽

Domain Specific Knowledge ◽

Visualization Systems ◽

User Friendly ◽

Machine Readable ◽

Associated Knowledge

Abstract. A key challenge in cultural heritage (CH) sites visualization is to provide models and tools that effectively integrate the content of a CH data with domain-specific knowledge so that the users can query, interpret and consume the visualized information. Moreover, it is important that the intelligent visualization systems are interoperable in the semantic web environment and thus, capable of establishing a methodology to acquire, integrate, analyze, generate and share numeric contents and associated knowledge in human and machine-readable Web. In this paper, we present a model, a methodology and a software Web-tools that support the coupling of the 2D/3D Web representation with the knowledge graph database of Xlendi shipwreck. The Web visualization tools and the knowledge-based techniques are married into a photogrammetry driven ontological model while at the same time, user-friendly web tools for querying and semantic consumption of the shipwreck information are introduced.

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Protein Structure Prediction

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch007 ◽

2019 ◽

pp. 156-184

Author(s):

Hirak Jyoti Chakraborty ◽

Aditi Gangopadhyay ◽

Sayak Ganguli ◽

Abhijit Datta

Keyword(s):

Protein Structure ◽

Ab Initio ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Future Research ◽

Computational Techniques ◽

Target Sequence ◽

Knowledge Based ◽

Research Areas ◽

Three Phases

The great disagreement between the number of known protein sequences and the number of experimentally determined protein structures indicate an enormous necessity of rapid and accurate protein structure prediction methods. Computational techniques such as comparative modeling, threading and ab initio modelling allow swift protein structure prediction with sufficient accuracy. The three phases of computational protein structure prediction comprise: the pre-modelling analysis phase, model construction and post-modelling refinement. Protein modelling is primarily comparative or ab initio. Comparative or template-based methods such as homology and threading-based modelling require structural templates for constructing the structure of a target sequence. The ab initio is a template-free modelling approach which proceeds by satisfying various physics-based and knowledge-based parameters. The chapter will elaborate on the three phases of modelling, the programs available for performing each, issues, possible solutions and future research areas.

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Protein Structure Prediction

Advances in Bioinformatics and Biomedical Engineering - Applying Big Data Analytics in Bioinformatics and Medicine ◽

10.4018/978-1-5225-2607-0.ch003 ◽

2018 ◽

pp. 48-79 ◽

Cited By ~ 1

Author(s):

Hirak Jyoti Chakraborty ◽

Aditi Gangopadhyay ◽

Sayak Ganguli ◽

Abhijit Datta

Keyword(s):

Protein Structure ◽

Ab Initio ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Future Research ◽

Computational Techniques ◽

Target Sequence ◽

Knowledge Based ◽

Research Areas ◽

Three Phases

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KORP: knowledge-based 6D potential for fast protein and loop modeling

Bioinformatics ◽

10.1093/bioinformatics/btz026 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3013-3019 ◽

Cited By ~ 13

Author(s):

José Ramón López-Blanco ◽

Pablo Chacón

Keyword(s):

Structure Prediction ◽

Protein Structures ◽

Joint Probability ◽

Protein Modeling ◽

Supplementary Information ◽

Joint Probability Distribution ◽

Loop Modeling ◽

Statistical Potentials ◽

Knowledge Based ◽

Backbone Atoms

Abstract Motivation Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue–residue interactions from known protein structures using a simple backbone-based representation. Results We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Using a minimalist representation of only three backbone atoms per residue, KORP utilizes a six-dimensional joint probability distribution to outperform state-of-the-art statistical potentials for native structure recognition and best model selection in recent critical assessment of protein structure prediction and loop-modeling benchmarks. Compared with the existing methods, our side-chain independent potential has a lower complexity and better efficiency. The superior accuracy and robustness of KORP represent a promising advance for protein modeling and refinement applications that require a fast but highly discriminative energy function. Availability and implementation http://chaconlab.org/modeling/korp. Supplementary information Supplementary data are available at Bioinformatics online.

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Smotifs as structural local descriptors of supersecondary elements: classification, completeness and applications

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2014-0016 ◽

2014 ◽

Vol 10 (4) ◽

Author(s):

Jaume Bonet ◽

Andras Fiser ◽

Baldo Oliva ◽

Narcis Fernandez-Fuentes

Keyword(s):

Protein Design ◽

Structure Prediction ◽

Protein Structures ◽

Regular Structure ◽

Loop Structure ◽

Apparent Lack ◽

Knowledge Based ◽

Limits Of Knowledge ◽

Folding Dynamics ◽

And Function

AbstractProtein structures are made up of periodic and aperiodic structural elements (i.e., α-helices, β-strands and loops). Despite the apparent lack of regular structure, loops have specific conformations and play a central role in the folding, dynamics, and function of proteins. In this article, we reviewed our previous works in the study of protein loops as local supersecondary structural motifs or Smotifs. We reexamined our works about the structural classification of loops (ArchDB) and its application to loop structure prediction (ArchPRED), including the assessment of the limits of knowledge-based loop structure prediction methods. We finalized this article by focusing on the modular nature of proteins and how the concept of Smotifs provides a convenient and practical approach to decompose proteins into strings of concatenated Smotifs and how can this be used in computational protein design and protein structure prediction.

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