scholarly journals A comparison of scoring functions for protein sequence profile alignment

2004 ◽  
Vol 20 (8) ◽  
pp. 1301-1308 ◽  
Author(s):  
R. C. Edgar ◽  
K. Sjolander
Keyword(s):  
Protein Sequence ◽  
Scoring Functions ◽  
Sequence Profile ◽  
Protein Sequence Profile ◽  
Profile Alignment

scholarly journals Protein sequence profile prediction using ProtAlbert transformer1

2021 ◽  
Author(s):  
Fatemeh Zare-Mirakabad ◽  
Armin Behjati ◽  
Seyed Shahriar Arab ◽  
Abbas Nowzari-Dalini
Keyword(s):  
Amino Acids ◽  
Protein Sequence ◽  
Nearest Neighbor ◽  
Tertiary Structure ◽  
Query Sequence ◽  
Protein Secondary Structure ◽  
Protein Sequences ◽  
Family Characteristics ◽  
Sequence Profile ◽  
Protein Sequence Profile

Protein sequences can be viewed as a language; therefore, we benefit from using the models initially developed for natural languages such as transformers. ProtAlbert is one of the best pre-trained transformers on protein sequences, and its efficiency enables us to run the model on longer sequences with less computation power while having similar performance with the other pre-trained transformers. This paper includes two main parts: transformer analysis and profile prediction. In the first part, we propose five algorithms to assess the attention heads in different layers of ProtAlbert for five protein characteristics, nearest-neighbor interactions, type of amino acids, biochemical and biophysical properties of amino acids, protein secondary structure, and protein tertiary structure. These algorithms are performed on 55 proteins extracted from CASP13 and three case study proteins whose sequences, experimental tertiary structures, and HSSP profiles are available. This assessment shows that although the model is only pre-trained on protein sequences, attention heads in the layers of ProtAlbert are representative of some protein family characteristics. This conclusion leads to the second part of our work. We propose an algorithm called PA_SPP for protein sequence profile prediction by pre-trained ProtAlbert using masked-language modeling. PA_SPP algorithm can help the researchers to predict an HSSP profile while there are no similar sequences to a query sequence in the database for making the HSSP profile.

scholarly journals To Improve Protein Sequence Profile Prediction through Image Captioning on Pairwise Residue Distance Map

2019 ◽  
Vol 60 (1) ◽  
pp. 391-399 ◽  
Author(s):  
Sheng Chen ◽  
Zhe Sun ◽  
Lihua Lin ◽  
Zifeng Liu ◽  
Xun Liu ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Image Captioning ◽  
Sequence Profile ◽  
Distance Map ◽  
Protein Sequence Profile

scholarly journals To Improve Protein Sequence Profile Prediction through Image Captioning on Pairwise Residue Distance Map

2019 ◽  
Author(s):  
Sheng Chen ◽  
Zhe Sun ◽  
Zifeng Liu ◽  
Xun Liu ◽  
Yutian Chong ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Network Architecture ◽  
Structural Information ◽  
3D Structure ◽  
Previous Method ◽  
Image Captioning ◽  
Sequence Profile ◽  
Distance Map ◽  
3D Structures ◽  
Protein Sequence Profile

ABSTRACTProtein sequence profile prediction aims to generate multiple sequences from structural information to advance the protein design. Protein sequence profile can be computationally predicted by energy-based method or fragment-based methods. By integrating these methods with neural networks, our previous method, SPIN2 has achieved a sequence recovery rate of 34%. However, SPIN2 employed only one dimensional (1D) structural properties that are not sufficient to represent 3D structures. In this study, we represented 3D structures by 2D maps of pairwise residue distances. and developed a new method (SPROF) to predict protein sequence profile based on an image captioning learning frame. To our best knowledge, this is the first method to employ 2D distance map for predicting protein properties. SPROF achieved 39.8% in sequence recovery of residues on the independent test set, representing a 5.2% improvement over SPIN2. We also found the sequence recovery increased with the number of their neighbored residues in 3D structural space, indicating that our method can effectively learn long range information from the 2D distance map. Thus, such network architecture using 2D distance map is expected to be useful for other 3D structure-based applications, such as binding site prediction, protein function prediction, and protein interaction prediction.

scholarly journals Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants

2021 ◽  
Author(s):  
Emidio Capriotti ◽  
Piero Fariselli
Keyword(s):  
Protein Sequence ◽  
Prediction Models ◽  
Evolutionary Information ◽  
Large Set ◽  
Primary Role ◽  
Sequence Profile ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Missense Variants ◽  
The Impact

Abstract Evolutionary information is the primary tool for detecting functional conservation in nucleic acid and protein. This information has been extensively used to predict structure, interactions and functions in macromolecules. Pathogenicity prediction models rely on multiple sequence alignment information at different levels. However, most accurate genome-wide variant deleteriousness ranking algorithms consider different features to assess the impact of variants. Here, we analyze three different ways of extracting evolutionary information from sequence alignments in the context of pathogenicity predictions at DNA and protein levels. We showed that protein sequence-based information is slightly more informative in the annotation of Clinvar missense variants than those obtained at the DNA level. Furthermore, to achieve the performance of state-of-the-art methods, such as CADD, the conservation of reference and variant, encoded as frequencies of reference/alternate alleles or wild-type/mutant residues, should be included. Our results on a large set of missense variants show that a basic method based on three input features derived from the protein sequence profile performs similarly to the CADD algorithm which uses hundreds of genomic features. This observation indicates that for missense variants, evolutionary information, when properly encoded, plays the primary role in ranking pathogenicity.

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