Refined template selection and combination algorithm significantly improves template-based modeling accuracy

2019 ◽  
Vol 17 (02) ◽  
pp. 1950006 ◽  
Author(s):  
Ashish Runthala ◽  
Shibasish Chowdhury
Keyword(s):  
Structural Information ◽  
Protein Structures ◽  
Comparative Modeling ◽  
Target Sequence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Modeling Accuracy ◽  
Model Protein ◽  
Rank And Select

In contrast to ab-initio protein modeling methodologies, comparative modeling is considered as the most popular and reliable algorithm to model protein structure. However, the selection of the best set of templates is still a major challenge. An effective template-ranking algorithm is developed to efficiently select only the reliable hits for predicting the protein structures. The algorithm employs the pairwise as well as multiple sequence alignments of template hits to rank and select the best possible set of templates. It captures several key sequences and structural information of template hits and converts into scores to effectively rank them. This selected set of templates is used to model a target. Modeling accuracy of the algorithm is tested and evaluated on TBM-HA domain containing CASP8, CASP9 and CASP10 targets. On an average, this template ranking and selection algorithm improves GDT-TS, GDT-HA and TM_Score by 3.531, 4.814 and 0.022, respectively. Further, it has been shown that the inclusion of structurally similar templates with ample conformational diversity is crucial for the modeling algorithm to maximally as well as reliably span the target sequence and construct its near-native model. The optimal model sampling also holds the key to predict the best possible target structure.

scholarly journals Characterization of Non-Trivial Neighborhood Fold Constraints from Protein Sequences using Generalized Topohydrophobicity.

2008 ◽  
Vol 2 ◽  
pp. BBI.S426 ◽  
Author(s):  
Guillaume Fourty ◽  
Isabelle Callebaut ◽  
Jean-Paul Mornon
Keyword(s):  
Secondary Structure ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Comparative Modeling ◽  
Local Geometry ◽  
Large Set ◽  
Structural Constraints ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

Prediction of key features of protein structures, such as secondary structure, solvent accessibility and number of contacts between residues, provides useful structural constraints for comparative modeling, fold recognition, ab-initio fold prediction and detection of remote relationships. In this study, we aim at characterizing the number of non-trivial close neighbors, or long-range contacts of a residue, as a function of its “topohydrophobic” index deduced from multiple sequence alignments and of the secondary structure in which it is embedded. The “topohydrophobic” index is calculated using a two-class distribution of amino acids, based on their mean atom depths. From a large set of structural alignments processed from the FSSP database, we selected 1485 structural sub-families including at least 8 members, with accurate alignments and limited redundancy. We show that residues within helices, even when deeply buried, have few non-trivial neighbors (0–2), whereas β-strand residues clearly exhibit a multimodal behavior, dominated by the local geometry of the tetrahedron (3 non-trivial close neighbors associated with one tetrahedron; 6 with two tetrahedra). This observed behavior allows the distinction, from sequence profiles, between edge and central β-strands within β-sheets. Useful topological constraints on the immediate neighborhood of an amino acid, but also on its correlated solvent accessibility, can thus be derived using this approach, from the simple knowledge of multiple sequence alignments.

scholarly journals Epistatic contributions promote the unification of incompatible models of neutral molecular evolution

2020 ◽  
Vol 117 (11) ◽  
pp. 5873-5882 ◽  
Author(s):  
Jose Alberto de la Paz ◽  
Charisse M. Nartey ◽  
Monisha Yuvaraj ◽  
Faruck Morcos
Keyword(s):  
Structural Information ◽  
Stokes Shift ◽  
Neutral Evolution ◽  
Emergent Properties ◽  
Sequence Evolution ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Analysis Methodology ◽  
The Relationship

We introduce a model of amino acid sequence evolution that accounts for the statistical behavior of real sequences induced by epistatic interactions. We base the model dynamics on parameters derived from multiple sequence alignments analyzed by using direct coupling analysis methodology. Known statistical properties such as overdispersion, heterotachy, and gamma-distributed rate-across-sites are shown to be emergent properties of this model while being consistent with neutral evolution theory, thereby unifying observations from previously disjointed evolutionary models of sequences. The relationship between site restriction and heterotachy is characterized by tracking the effective alphabet dynamics of sites. We also observe an evolutionary Stokes shift in the fitness of sequences that have undergone evolution under our simulation. By analyzing the structural information of some proteins, we corroborate that the strongest Stokes shifts derive from sites that physically interact in networks near biochemically important regions. Perspectives on the implementation of our model in the context of the molecular clock are discussed.

scholarly journals Fold and flexibility: what can proteins' mechanical properties tell us about their folding nucleus?

2015 ◽  
Vol 12 (112) ◽  
pp. 20150876 ◽  
Author(s):  
Sophie Sacquin-Mora
Keyword(s):  
Mechanical Properties ◽  
Brownian Dynamics ◽  
Protein Structures ◽  
Interaction Network ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Folding Nucleus ◽  
Dynamics Simulations ◽  
Brownian Dynamics Simulations

The determination of a protein's folding nucleus, i.e. a set of native contacts playing an important role during its folding process, remains an elusive yet essential problem in biochemistry. In this work, we investigate the mechanical properties of 70 protein structures belonging to 14 protein families presenting various folds using coarse-grain Brownian dynamics simulations. The resulting rigidity profiles combined with multiple sequence alignments show that a limited set of rigid residues, which we call the consensus nucleus, occupy conserved positions along the protein sequence. These residues' side chains form a tight interaction network within the protein's core, thus making our consensus nuclei potential folding nuclei. A review of experimental and theoretical literature shows that most (above 80%) of these residues were indeed identified as folding nucleus member in earlier studies.

scholarly journals SPOT-1D-LM: Reaching Alignment-profile-based Accuracy in Predicting Protein Secondary and Tertiary Structural Properties without Alignment.

2021 ◽  
Author(s):  
Jaspreet Singh ◽  
Kuldip Paliwal ◽  
Jaswinder Singh ◽  
Yaoqi Zhou
Keyword(s):  
Structural Properties ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Language Models ◽  
Sequence Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Structural And Functional Properties ◽  
Sequence Profiles

Protein language models have emerged as an alternative to multiple sequence alignment for enriching sequence information and improving downstream prediction tasks such as biophysical, structural, and functional properties. Here we show that a combination of traditional one-hot encoding with the embeddings from two different language models (ProtTrans and ESM-1b) allows a leap in accuracy over single-sequence based techniques in predicting protein 1D secondary and tertiary structural properties, including backbone torsion angles, solvent accessibility and contact numbers. This large improvement leads to an accuracy comparable to or better than the current state-of-the-art techniques for predicting these 1D structural properties based on sequence profiles generated from multiple sequence alignments. The high-accuracy prediction in both secondary and tertiary structural properties indicates that it is possible to make highly accurate prediction of protein structures without homologous sequences, the remaining obstacle in the post AlphaFold2 era.

scholarly journals Analysis of several key factors influencing deep learning-based inter-residue contact prediction

2019 ◽  
Author(s):  
Tianqi Wu ◽  
Jie Hou ◽  
Badri Adhikari ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Structural Information ◽  
Protein Structures ◽  
Supplementary Information ◽  
Prediction Methods ◽  
Key Factors ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Ab Initio Approach

Abstract Motivation Deep learning has become the dominant technology for protein contact prediction. However, the factors that affect the performance of deep learning in contact prediction have not been systematically investigated. Results We analyzed the results of our three deep learning-based contact prediction methods (MULTICOM-CLUSTER, MULTICOM-CONSTRUCT and MULTICOM-NOVEL) in the CASP13 experiment and identified several key factors [i.e. deep learning technique, multiple sequence alignment (MSA), distance distribution prediction and domain-based contact integration] that influenced the contact prediction accuracy. We compared our convolutional neural network (CNN)-based contact prediction methods with three coevolution-based methods on 75 CASP13 targets consisting of 108 domains. We demonstrated that the CNN-based multi-distance approach was able to leverage global coevolutionary coupling patterns comprised of multiple correlated contacts for more accurate contact prediction than the local coevolution-based methods, leading to a substantial increase of precision by 19.2 percentage points. We also tested different alignment methods and domain-based contact prediction with the deep learning contact predictors. The comparison of the three methods showed deeper sequence alignments and the integration of domain-based contact prediction with the full-length contact prediction improved the performance of contact prediction. Moreover, we demonstrated that the domain-based contact prediction based on a novel ab initio approach of parsing domains from MSAs alone without using known protein structures was a simple, fast approach to improve contact prediction. Finally, we showed that predicting the distribution of inter-residue distances in multiple distance intervals could capture more structural information and improve binary contact prediction. Availability and implementation https://github.com/multicom-toolbox/DNCON2/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns

2013 ◽  
Vol 29 (17) ◽  
pp. 2112-2121 ◽  
Author(s):  
Francisco M. Ortuño ◽  
Olga Valenzuela ◽  
Fernando Rojas ◽  
Hector Pomares ◽  
Javier P. Florido ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Structural Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

scholarly journals Bioinformatic analysis of riboswitch structures uncovers variant classes with altered ligand specificity

2017 ◽  
Vol 114 (11) ◽  
pp. E2077-E2085 ◽  
Author(s):  
Zasha Weinberg ◽  
James W. Nelson ◽  
Christina E. Lünse ◽  
Madeline E. Sherlock ◽  
Ronald R. Breaker
Keyword(s):  
Structural Information ◽  
Bioinformatic Analysis ◽  
Flavin Mononucleotide ◽  
Ligand Specificity ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Form Complex ◽  
Multiple Sequence Alignments ◽  
Bacterial Signaling ◽  
Folded Structures

Riboswitches are RNAs that form complex, folded structures that selectively bind small molecules or ions. As with certain groups of protein enzymes and receptors, some riboswitch classes have evolved to change their ligand specificity. We developed a procedure to systematically analyze known riboswitch classes to find additional variants that have altered their ligand specificity. This approach uses multiple-sequence alignments, atomic-resolution structural information, and riboswitch gene associations. Among the discoveries are unique variants of the guanine riboswitch class that most tightly bind the nucleoside 2′-deoxyguanosine. In addition, we identified variants of the glycine riboswitch class that no longer recognize this amino acid, additional members of a rare flavin mononucleotide (FMN) variant class, and also variants of c-di-GMP-I and -II riboswitches that might recognize different bacterial signaling molecules. These findings further reveal the diverse molecular sensing capabilities of RNA, which highlights the potential for discovering a large number of additional natural riboswitch classes.

scholarly journals Distillation of MSA Embeddings to Folded Protein Structures with Graph Transformers

2021 ◽  
Author(s):  
Allan Costa ◽  
Manvitha Ponnapati ◽  
Joseph M Jacobson ◽  
Pranam Chatterjee
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Sequences ◽  
Language Models ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Folded Structures

Determining the structure of proteins has been a long-standing goal in biology. Language models have been recently deployed to capture the evolutionary semantics of protein sequences. Enriched with multiple sequence alignments (MSA), these models can encode protein tertiary structure. In this work, we introduce an attention-based graph architecture that exploits MSA Transformer embeddings to directly produce three-dimensional folded structures from protein sequences. We envision that this pipeline will provide a basis for efficient, end-to-end protein structure prediction.

scholarly journals Expresso: automatic incorporation of structural information in multiple sequence alignments using 3D-Coffee

2006 ◽  
Vol 34 (Web Server) ◽  
pp. W604-W608 ◽  
Author(s):  
F. Armougom ◽  
S. Moretti ◽  
O. Poirot ◽  
S. Audic ◽  
P. Dumas ◽  
...  
Keyword(s):  
Structural Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

scholarly journals Benchmarking inverse statistical approaches for protein structure and design with exactly solvable models

2015 ◽  
Author(s):  
Hugo Jacquin ◽  
Amy Gilson ◽  
Eugene Shakhnovich ◽  
Simona Cocco ◽  
Rémi Monasson
Keyword(s):  
Protein Structure ◽  
Structural Information ◽  
Sequence Data ◽  
Careful Analysis ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Pairwise Models ◽  
Statistical Approaches ◽  
And Function

Inverse statistical approaches to determine protein structure and function from Multiple Sequence Alignments (MSA) are emerging as powerful tools in computational biology. However the underlying assumptions of the relationship between the inferred effective Potts Hamiltonian and real protein structure and energetics remain untested so far. Here we use lattice protein model (LP) to benchmark those inverse statistical approaches. We build MSA of highly stable sequences in target LP structures, and infer the effective pairwise Potts Hamiltonians from those MSA. We find that inferred Potts Hamiltonians reproduce many important aspects of `true' LP structures and energetics. Careful analysis reveals that effective pairwise couplings in inferred Potts Hamiltonians depend not only on the energetics of the native structure but also on competing folds; in particular, the coupling values reflect both positive design (stabilization of native conformation) and negative design (destabilization of competing folds). In addition to providing detailed structural information, the inferred Potts models used as protein Hamiltonian for design of new sequences are able to generate with high probability completely new sequences with the desired folds, which is not possible using independent-site models. Those are remarkable results as the effective LP Hamiltonians used to generate MSA are not simple pairwise models due to the competition between the folds. Our findings elucidate the reasons of the power of inverse approaches to the modelling of proteins from sequence data, and their limitations; we show, in particular, that their success crucially depend on the accurate inference of the Potts pairwise couplings.

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