Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

/pdf/protein-secondary-structure-prediction-using-deep-55j403d6bh.pdf

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Protein structure alignment is a fundamental problem in computational structure biology. Many programs have been developed for automatic protein structure alignment, but most of them align two protein structures purely based upon geometric similarity without considering evolutionary and functional relationship. As such, these programs may generate structure alignments which are not very biologically meaningful from the evolutionary perspective. This paper presents a novel method DeepAlign for automatic pairwise protein structure alignment. DeepAlign aligns two protein structures using not only spatial proximity of equivalent residues (after rigid-body superposition), but also evolutionary relationship and hydrogen-bonding similarity. Experimental results show that DeepAlign can generate structure alignments much more consistent with manually-curated alignments than other automatic tools especially when proteins under consideration are remote homologs. These results imply that in addition to geometric similarity, evolutionary information and hydrogen-bonding similarity are essential to aligning two protein structures.

/pdf/protein-structure-alignment-beyond-spatial-proximity-2lf3jb914s.pdf

Protein structure alignment beyond spatial proximity

Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields), to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.

/pdf/deepcnf-d-predicting-protein-order-disorder-regions-by-1jgwhf0yyg.pdf

DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

Motivation: Building an accurate alignment of a large set of distantly related protein structures is still very challenging.

Results: This article presents a novel method 3DCOMB that can generate a multiple structure alignment (MSA) with not only as many conserved cores as possible, but also high-quality pairwise alignments. 3DCOMB is unique in that it makes use of both local and global structure environments, combined by a statistical learning method, to accurately identify highly similar fragment blocks (HSFBs) among all proteins to be aligned. By extending the alignments of these HSFBs, 3DCOMB can quickly generate an accurate MSA without using progressive alignment. 3DCOMB significantly excels others in aligning distantly related proteins. 3DCOMB can also generate correct alignments for functionally similar regions among proteins of very different structures while many other MSA tools fail. 3DCOMB is useful for many real-world applications. In particular, it enables us to find out that there is still large improvement room for multiple template homology modeling while several other MSA tools fail to do so.

Availability: 3DCOMB is available at http://ttic.uchicago.edu/~jinbo/software.htm.

Contact: [email protected]

Supplementary Information:Supplementary data are available at Bioinformatics online.

/pdf/alignment-of-distantly-related-protein-structures-44yywx07c5.pdf

Alignment of distantly related protein structures

As a fundamental problem in computational structure biology, protein structure alignment has attracted the focus of the community for more than 20 years. While the pairwise structure alignment could be applied to measure the similarity between two proteins, which is a first step for homology search and fold space construction, the multiple structure alignment could be used to understand evolutionary conservation and divergence from a family of protein structures. Structure alignment is an NP-hard problem, which is only computationally tractable by using heuristics. Three levels of heuristics for pairwise structure alignment have been proposed, from the representations of protein structure, the perspectives of viewing protein as a rigid-body or flexible, to the scoring functions as well as the search algorithms for the alignment. For multiple structure alignment, the fourth level of heuristics is applied on how to merge all input structures to a multiple structure alignment. In this review, we first present a small survey of current methods for protein pairwise and multiple alignment, focusing on those that are publicly available as web servers. In more detail, we also discuss the advancements on the development of the new approaches to increase the pairwise alignment accuracy, to efficiently and reliably merge input structures to the multiple structure alignment. Finally, besides broadening the spectrum of the applications of structure alignment for protein template-based prediction, we also list several open problems that need to be solved in the future, such as the large complex alignment and the fast database search.

Algorithms, applications, and challenges of protein structure alignment.

Most approaches for protein structure alignment start from a search for similar fragments since this local similarity is necessary to the alignment even though is insufficient. In contrary to the sequence alignment, any insignificant trial alignment for structures can be detected by structure superposition and then excluded. It is then practicable to select from locally similar fragments those responsible for alignment and build up it. An efficient way for local similarity search is to use a conformational alphabet, which is a discretized description of protein chain local geometry. Using our conformational alphabet and its substitution matrix CLESUM, we propose a tool called BLOMAPS for fast multiple structure alignment. By means of the conformational alphabet, a structural fragment is mapped to a string, and two strings with their CLESUM score being higher than a preset threshold form a similar fragment pair (SFP). A string from one protein as a seed and its highly similar fragments from other proteins form a similar fragment block. Taking one protein as the pivot, BLOMAPS uses the rigid transformation for SFPs in a block to superimpose proteins and initiate an anchor-based alignment. BLOMAPS is greedy in nature, guided by CLESUM similarity scores. It consists of several steps including finding similar fragment blocks based on a pivot protein, removing block redundancy, constructing scaffold by checking consistency in spatial arrangement among fragments from different blocks, dealing with unanchored structures, and the final step of refinement where the average template for alignment is obtained and motifs missing from the pivot protein are found and added. The utility of BLOMAPS is tested on various protein structure ensembles including large scale ones, and compared with several other tools including MATT.

/pdf/fast-multiple-alignment-of-protein-structures-using-4q3q2od3by.pdf

Fast Multiple Alignment of Protein Structures Using Conformational Letter Blocks

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Wang Sheng

Author Tools

Chat about Author

Papers

Fast Multiple Alignment of Protein Structures Using Conformational Letter Blocks