scholarly journals Evaluation of residue-residue contact prediction methods: From retrospective to prospective

2021 ◽  
Vol 17 (5) ◽  
pp. e1009027
Author(s):  
Huiling Zhang ◽  
Zhendong Bei ◽  
Wenhui Xi ◽  
Min Hao ◽  
Zhen Ju ◽  
...  
Keyword(s):  
Hydrophobic Interactions ◽  
Great Success ◽  
Sequence Alignments ◽  
Contact Density ◽  
Data Set ◽  
Contact Prediction ◽  
Intrinsically Disordered ◽  
Residue Contact ◽  
Wide Range ◽  
Training Sets

Sequence-based residue contact prediction plays a crucial role in protein structure reconstruction. In recent years, the combination of evolutionary coupling analysis (ECA) and deep learning (DL) techniques has made tremendous progress for residue contact prediction, thus a comprehensive assessment of current methods based on a large-scale benchmark data set is very needed. In this study, we evaluate 18 contact predictors on 610 non-redundant proteins and 32 CASP13 targets according to a wide range of perspectives. The results show that different methods have different application scenarios: (1) DL methods based on multi-categories of inputs and large training sets are the best choices for low-contact-density proteins such as the intrinsically disordered ones and proteins with shallow multi-sequence alignments (MSAs). (2) With at least 5L (L is sequence length) effective sequences in the MSA, all the methods show the best performance, and methods that rely only on MSA as input can reach comparable achievements as methods that adopt multi-source inputs. (3) For top L/5 and L/2 predictions, DL methods can predict more hydrophobic interactions while ECA methods predict more salt bridges and disulfide bonds. (4) ECA methods can detect more secondary structure interactions, while DL methods can accurately excavate more contact patterns and prune isolated false positives. In general, multi-input DL methods with large training sets dominate current approaches with the best overall performance. Despite the great success of current DL methods must be stated the fact that there is still much room left for further improvement: (1) With shallow MSAs, the performance will be greatly affected. (2) Current methods show lower precisions for inter-domain compared with intra-domain contact predictions, as well as very high imbalances in precisions for intra-domains. (3) Strong prediction similarities between DL methods indicating more feature types and diversified models need to be developed. (4) The runtime of most methods can be further optimized.

scholarly journals Chasing coevolutionary signals in intrinsically disordered proteins complexes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Javier A. Iserte ◽  
Tamas Lazar ◽  
Silvio C. E. Tosatto ◽  
Peter Tompa ◽  
Cristina Marino-Buslje
Keyword(s):  
Protein Interactions ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Protein Protein Interactions ◽  
Contact Prediction ◽  
Intrinsically Disordered ◽  
Regulatory Processes ◽  
Wide Range ◽  
Predict Protein Structure ◽  
Biological Interpretation

Abstract Intrinsically disordered proteins/regions (IDPs/IDRs) are crucial components of the cell, they are highly abundant and participate ubiquitously in a wide range of biological functions, such as regulatory processes and cell signaling. Many of their important functions rely on protein interactions, by which they trigger or modulate different pathways. Sequence covariation, a powerful tool for protein contact prediction, has been applied successfully to predict protein structure and to identify protein–protein interactions mostly of globular proteins. IDPs/IDRs also mediate a plethora of protein–protein interactions, highlighting the importance of addressing sequence covariation-based inter-protein contact prediction of this class of proteins. Despite their importance, a systematic approach to analyze the covariation phenomena of intrinsically disordered proteins and their complexes is still missing. Here we carry out a comprehensive critical assessment of coevolution-based contact prediction in IDP/IDR complexes and detail the challenges and possible limitations that emerge from their analysis. We found that the coevolutionary signal is faint in most of the complexes of disordered proteins but positively correlates with the interface size and binding affinity between partners. In addition, we discuss the state-of-art methodology by biological interpretation of the results, formulate evaluation guidelines and suggest future directions of development to the field.

scholarly journals Distance-based Reconstruction of Protein Quaternary Structures from Inter-Chain Contacts

2021 ◽  
Author(s):  
Elham Soltanikazemi ◽  
Farhan Quadir ◽  
Raj Roy ◽  
Jianlin Cheng
Keyword(s):  
Quaternary Structure ◽  
Structural Models ◽  
Simulation Method ◽  
Contact Density ◽  
High Quality ◽  
Contact Prediction ◽  
Residue Contact ◽  
Protein Dimers ◽  
Distance Restraints ◽  
Quaternary Structures

Predicting the quaternary structure of protein complex is an important problem. Inter-chain residue-residue contact prediction can provide useful information to guide the ab initio reconstruction of quaternary structures. However, few methods have been developed to build quaternary structures from predicted inter-chain contacts. Here, we introduce a gradient descent optimization algorithm (GD) to build quaternary structures of protein dimers utilizing inter-chain contacts as distance restraints. We evaluate GD on several datasets of homodimers and heterodimers using true or predicted contacts. GD consistently performs better than a simulated annealing method and a Markov Chain Monte Carlo simulation method. Using true inter-chain contacts as input, GD can reconstruct high-quality structural models for homodimers and heterodimers with average TM-score ranging from 0.92 to 0.99 and average interface root mean square distance (I-RMSD) from 0.72 Å to 1.64 Å. On a dataset of 115 homodimers, using predicted inter-chain contacts as input, the average TM-score of the structural models built by GD is 0.76. For 46% of the homodimers, high-quality structural models with TM-score >= 0.9 are reconstructed from predicted contacts. There is a strong correlation between the quality of the reconstructed models and the precision and recall of predicted contacts. If the precision or recall of predicted contacts is >20%, GD can reconstruct good models for most homodimers, indicating only a moderate precision or recall of inter-chain contact prediction is needed to build good structural models for most homodimers. Moreover, the accuracy of reconstructed models positively correlates with the contact density in dimers.

scholarly journals Predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning

2020 ◽  
Author(s):  
Farhan Quadir ◽  
Raj Roy ◽  
Randal Halfmann ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Tertiary Structure ◽  
Protein Complexes ◽  
Great Success ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Residue Contact ◽  
Residue Contacts ◽  
Evolutionary Features

AbstractDeep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain contacts between proteins. However, these methods require multiple sequence alignments (MSAs) of a pair of interacting proteins (dimers) as input, which are often difficult to obtain because there are not many known protein complexes available to generate MSAs of sufficient depth for a pair of proteins. In recognizing that multiple sequence alignments of a monomer that forms homomultimers contain the co-evolutionary signals of both intrachain and interchain residue pairs in contact, we applied DNCON2 (a deep learning-based protein intrachain residue-residue contact predictor) to predict both intrachain and interchain contacts for homomultimers using multiple sequence alignment (MSA) and other co-evolutionary features of a single monomer followed by discrimination of interchain and intrachain contacts according to the tertiary structure of the monomer. Allowing true-positive predictions within two residue shifts, the best average precision was obtained for the Top-L/10 predictions of DNCON2: 22.9% for homodimers, and 17.0% for higher order homomultimers. In some instances, especially where interchain contact densities are high, the approach predicted interchain contacts with 100% precision. We show that the predicted contacts can be used to accurately construct the structure of some complexes. Our experiment demonstrates that monomeric multiple sequence alignments can be used with deep learning to predict interchain contacts of homomeric proteins.

Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

2019 ◽  
Vol 16 (7) ◽  
pp. 808-817 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Aromatase Inhibitors ◽  
Direct Synthesis ◽  
3D Qsar ◽  
Chemotherapeutic Agents ◽  
Modeling Tools ◽  
Data Set ◽  
Wide Range ◽  
Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals In Vitro Cellular Uptake Studies of Self-Assembled Fluorinated Nanoparticles Labelled with Antibodies

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1906
Author(s):  
Mona Atabakhshi-Kashi ◽  
Mónica Carril ◽  
Hossein Mahdavi ◽  
Wolfgang J. Parak ◽  
Carolina Carrillo-Carrion ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Cellular Uptake ◽  
Hydrophobic Interactions ◽  
Intrinsic Properties ◽  
Wide Range ◽  
Self Assembled ◽  
Uptake Studies ◽  
Targeting Ability ◽  
Fluorinated Nanoparticles

Nanoparticles (NPs) functionalized with antibodies (Abs) on their surface are used in a wide range of bioapplications. Whereas the attachment of antibodies to single NPs to trigger the internalization in cells via receptor-mediated endocytosis has been widely studied, the conjugation of antibodies to larger NP assemblies has been much less explored. Taking into account that NP assemblies may be advantageous for some specific applications, the possibility of incorporating targeting ligands is quite important. Herein, we performed the effective conjugation of antibodies onto a fluorescent NP assembly, which consisted of fluorinated Quantum Dots (QD) self-assembled through fluorine–fluorine hydrophobic interactions. Cellular uptake studies by confocal microscopy and flow cytometry revealed that the NP assembly underwent the same uptake procedure as individual NPs; that is, the antibodies retained their targeting ability once attached to the nanoassembly, and the NP assembly preserved its intrinsic properties (i.e., fluorescence in the case of QD nanoassembly).

scholarly journals Whole genome-based characterisation of antimicrobial resistance and genetic diversity in Campylobacter jejuni and Campylobacter coli from ruminants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Medelin Ocejo ◽  
Beatriz Oporto ◽  
José Luis Lavín ◽  
Ana Hurtado
Keyword(s):  
Genetic Diversity ◽  
Antimicrobial Resistance ◽  
Campylobacter Jejuni ◽  
Resistance Genes ◽  
Campylobacter Coli ◽  
Whole Genome ◽  
Northern Spain ◽  
Sequence Alignments ◽  
Phenotypic Data ◽  
Wide Range

AbstractCampylobacter, a leading cause of gastroenteritis in humans, asymptomatically colonises the intestinal tract of a wide range of animals.Although antimicrobial treatment is restricted to severe cases, the increase of antimicrobial resistance (AMR) is a concern. Considering the significant contribution of ruminants as reservoirs of resistant Campylobacter, Illumina whole-genome sequencing was used to characterise the mechanisms of AMR in Campylobacter jejuni and Campylobacter coli recovered from beef cattle, dairy cattle, and sheep in northern Spain. Genome analysis showed extensive genetic diversity that clearly separated both species. Resistance genotypes were identified by screening assembled sequences with BLASTn and ABRicate, and additional sequence alignments were performed to search for frameshift mutations and gene modifications. A high correlation was observed between phenotypic resistance to a given antimicrobial and the presence of the corresponding known resistance genes. Detailed sequence analysis allowed us to detect the recently described mosaic tet(O/M/O) gene in one C. coli, describe possible new alleles of blaOXA-61-like genes, and decipher the genetic context of aminoglycoside resistance genes, as well as the plasmid/chromosomal location of the different AMR genes and their implication for resistance spread. Updated resistance gene databases and detailed analysis of the matched open reading frames are needed to avoid errors when using WGS-based analysis pipelines for AMR detection in the absence of phenotypic data.

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