CN110706741A - Multi-modal protein structure prediction method based on sequence niche - Google Patents

Multi-modal protein structure prediction method based on sequence niche Download PDF

Info

Publication number
CN110706741A
CN110706741A CN201910793341.1A CN201910793341A CN110706741A CN 110706741 A CN110706741 A CN 110706741A CN 201910793341 A CN201910793341 A CN 201910793341A CN 110706741 A CN110706741 A CN 110706741A
Authority
CN
China
Prior art keywords
conformation
energy
sequence
rosetta
target
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201910793341.1A
Other languages
Chinese (zh)
Other versions
CN110706741B (en
Inventor
张贵军
夏瑜豪
饶亮
刘俊
彭春祥
周晓根
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University of Technology ZJUT
Original Assignee
Zhejiang University of Technology ZJUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University of Technology ZJUT filed Critical Zhejiang University of Technology ZJUT
Priority to CN201910793341.1A priority Critical patent/CN110706741B/en
Publication of CN110706741A publication Critical patent/CN110706741A/en
Application granted granted Critical
Publication of CN110706741B publication Critical patent/CN110706741B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/20—Protein or domain folding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B35/00—ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
    • G16B35/10—Design of libraries

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Biotechnology (AREA)
  • Theoretical Computer Science (AREA)
  • Biophysics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Evolutionary Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Library & Information Science (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Biochemistry (AREA)
  • Molecular Biology (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

A multi-modal protein structure prediction method based on sequence niches is characterized in that under a Monte Carlo framework, an original energy function is used for first round search; and then, an energy function for the next operation is constructed according to the conformation information obtained after each operation, so that the situation that the conformations are repeatedly trapped in the same energy trap is avoided, the problem of inaccuracy of the energy function can be relieved, the sampling capability can be enhanced, the sampling efficiency is improved, and the prediction precision is improved. The invention provides a sequence niche-based multi-modal protein structure prediction method with high prediction accuracy.

Description

Multi-modal protein structure prediction method based on sequence niche
Technical Field
The invention relates to the fields of bioinformatics and computer application, in particular to a sequence niche-based multi-modal protein structure prediction method.
Background
Protein molecules are central to many biochemical processes in cells. They are produced, mobilized, and killed in cells in precise time and space, and perform their functions necessary to sustain the life activities of the organism, depending on the three-dimensional structure of these proteins. Therefore, how to accurately obtain the three-dimensional structure of the protein and elucidate the relationship between the three-dimensional structure and the biological function is a serious challenge.
Currently, methods of biological wet experiments, such as X-ray diffraction, nuclear magnetic resonance, and cryoelectron microscopy, are mainly used to determine the three-dimensional structure of proteins. X-ray diffraction is the most effective method for determining protein structure at present, the accuracy achieved by the method is incomparable with other methods, and the main defects are that protein crystals are difficult to culture and the period for determining the crystal structure is long; the nuclear magnetic resonance method can directly measure the structure of the protein in the solution, but has large requirements on the sample quantity and high purity, and only can measure the small-molecular protein at present. The main problems of the experimental determination of structure method are two aspects: on the one hand, it is difficult to determine the structure of membrane proteins, the main targets of modern drug design; in addition, the experimental determination process is time consuming, expensive, and costly, e.g., using nmr methods to determine a protein structure typically requires 15 thousand dollars and a half year of time. Furthermore, the speed of measurement by the experimental method is far from the speed of sequence measurement. Therefore, an efficient, fast and simple method for predicting the structure of unknown protein is urgently needed. Anfinsen suggested in 1961 that the amino acid sequence of a protein determines its spatial arrangement for biological activity. Therefore, a method for predicting the three-dimensional structure of a protein from its amino acid sequence by computer technology has been proposed. Methods for predicting the three-dimensional structure of a protein based on an amino acid sequence mainly include a homology modeling method and a de novo prediction method. The de novo prediction method searches a globally optimal solution in a conformational space using an optimization algorithm based directly on a physical or knowledge energy model of the protein.
However, the conformational space of proteins is extremely large and complex, and the existing methods often have three major disadvantages: first, the energy function is not precise, and the low-energy conformation is not necessarily closer to the native structure, resulting in failure to accurately find satisfactory results; secondly, the sampling capability of the current optimization method is insufficient, and the energy barrier is difficult to cross in the sampling process, so that the searched conformation is limited in a potential energy trap, and the overall prediction accuracy is influenced; thirdly, the traditional monte carlo method needs to reverse the weight every time the operation is performed, so that a plurality of tracks are completely independent and can not acquire information, and the conformation obtained after multiple operations is easy to fall into the same trap repeatedly, and the conformation with various structures is difficult to obtain.
Therefore, the existing protein structure prediction method has the problems of inaccurate energy function, insufficient sampling capability, low sampling efficiency, insufficient prediction accuracy and the like, and needs to be improved.
Disclosure of Invention
In order to solve the problems of inaccurate energy function, insufficient sampling capability, low sampling efficiency, insufficient prediction precision and the like of the conventional protein structure prediction method, the invention provides a sequence niche-based multi-mode protein structure prediction method, which constructs an energy function for next operation according to conformation information obtained after each operation by serially operating a plurality of Monte Carlo tracks, so that the conformation is prevented from being trapped in a trap of the previous round, the sampling capability is enhanced, the sampling efficiency is improved, and the overall prediction precision is improved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for sequence niche-based multi-modal protein structure prediction, the method comprising the steps of:
1) inputting sequence information of a target protein;
2) acquiring fragment library files of 3 fragments and 9 fragments from a ROBETTA server (http:// www.robetta.org /) according to a target protein sequence;
3) setting parameters: maximum iteration times G, an energy function coefficient k and a degradation function coefficient m;
4) setting G ═ 1, G ∈ {1, 2.., G };
5) and (3) conformation initialization: generating an initial constellation using the first and second phases of the Rosetta protocol
Figure BDA0002180121910000022
If g is 1, continue with step 6); otherwise, go to step 7);
6) the initial modal conformation generation operation is as follows:
6.1) recording P as the target conformation to
Figure BDA0002180121910000023
Running the fourth phase of the Rosetta protocol as the initial constellation and setting the energy function M in Rosettag(P) score3(P), noteThe receiving constellation with the highest energy in the fourth phase of Rosetta
Figure BDA0002180121910000025
Figure BDA0002180121910000026
The lowest energy receiving conformation;
6.2) notesRespectively, the lowest energy receiving conformation
Figure BDA0002180121910000028
The dihedral angle of the ith residue of (1),
Figure BDA0002180121910000029
respectively, the highest energy receiving conformation
Figure BDA00021801219100000210
L is the length of the sequence of the target protein, and the radius of niche r is calculated as followsg：
Figure BDA0002180121910000021
6.3) performing step 8);
7) the multimodal conformation generation procedure is as follows:
7.1) noting P as the target conformation,
Figure BDA00021801219100000311
φi、ωidihedral angles, M, of the i-th residue of the target conformation P, respectivelyg(P) is the energy function of the g-th iteration,
Figure BDA0002180121910000034
rg-1respectively the highest energy value, the lowest energy conformation and the niche radius of the g-1 iteration,
Figure BDA0002180121910000035
is the distance between the target conformation and the energy-minimum conformation, to
Figure BDA0002180121910000036
The fourth stage of the Rosetta protocol was performed as the initial constellation and the energy function was calculated as follows:
Figure BDA0002180121910000031
Figure BDA0002180121910000032
Figure BDA0002180121910000033
7.2) notes
Figure BDA0002180121910000037
The receiving constellation with the highest energy in the fourth phase of Rosetta
Figure BDA0002180121910000038
Figure BDA0002180121910000039
Calculating the niche radius r for the lowest energy receiving constellation according to the formula (1) in step 6.2)g；
8) Setting G to G +1, and if G > G, executing step 9); otherwise, turning to the step 5);
9) outputting G energy-lowest constellations in G iterations
Figure BDA00021801219100000310
As a final prediction result, G ∈ {1, 2.
The technical conception of the invention is as follows: under the monte carlo framework, firstly, a first round of search is carried out by using an original energy function; then, constructing an energy function for the next operation according to the conformation information obtained after each operation, and avoiding the repeated trapping of the conformation into the same energy trap; finally, the lowest energy conformation in each run is output as the final prediction. The multi-modal protein structure prediction method based on the sequence niche can not only relieve the problem of inaccurate energy function, but also enhance the sampling capability and improve the sampling efficiency, thereby improving the prediction precision.
The invention has the beneficial effects that: according to the sequence niche strategy, the sampling efficiency is improved; outputting multiple conformations alleviates the drawback of evaluating conformations with only a single energy function, increases conformational diversity, and thus improves overall prediction accuracy.
Drawings
FIG. 1 is a schematic diagram of conformation update when a multi-modal protein structure prediction method based on sequence niches performs structure prediction on protein 1 FNA.
FIG. 2 is a three-dimensional structure diagram obtained by performing structure prediction on protein 1FNA by a multi-mode protein structure prediction method based on sequence niches.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 and 2, a method for multi-modal protein structure prediction based on sequence niches, the method comprising the steps of:
1) inputting sequence information of a target protein;
2) acquiring fragment library files of 3 fragments and 9 fragments from a ROBETTA server (http:// www.robetta.org /) according to a target protein sequence;
3) setting parameters: maximum iteration times G, an energy function coefficient k and a degradation function coefficient m;
4) setting G ═ 1, G ∈ {1, 2.., G };
5) and (3) conformation initialization: generating an initial constellation using the first and second phases of the Rosetta protocolIf g is 1, continue with step 6); otherwise, go to step 7);
6) the initial modal conformation generation operation is as follows:
6.1) recording P as the target conformation to
Figure BDA0002180121910000043
Running the fourth phase of the Rosetta protocol as the initial constellation and setting the energy function M in Rosettag(P) score3(P), note
Figure BDA0002180121910000044
The receiving constellation with the highest energy in the fourth phase of Rosetta
Figure BDA0002180121910000045
Figure BDA0002180121910000046
The lowest energy receiving conformation;
6.2) notes
Figure BDA0002180121910000047
Respectively, the lowest energy receiving conformation
Figure BDA0002180121910000048
Residue i of (2)The angle of the two-sided angle of (c),
Figure BDA0002180121910000049
respectively, the highest energy receiving conformation
Figure BDA00021801219100000410
L is the length of the sequence of the target protein, and the radius of niche r is calculated as followsg：
Figure BDA0002180121910000041
6.3) performing step 8);
7) the multimodal conformation generation procedure is as follows:
7.1) noting P as the target conformation,
Figure BDA00021801219100000414
φi、ωidihedral angles, M, of the i-th residue of the target conformation P, respectivelyg(P) is the energy function of the g-th iteration,
Figure BDA00021801219100000411
rg-1respectively the highest energy value, the lowest energy conformation and the niche radius of the g-1 iteration,
Figure BDA00021801219100000412
is the distance between the target conformation and the energy-minimum conformation, to
Figure BDA00021801219100000413
The fourth stage of the Rosetta protocol was performed as the initial constellation and the energy function was calculated as follows:
Figure BDA0002180121910000052
Figure BDA0002180121910000053
7.2) notes
Figure BDA0002180121910000055
The receiving constellation with the highest energy in the fourth phase of Rosetta
Figure BDA0002180121910000056
Figure BDA0002180121910000057
Calculating the niche radius r for the lowest energy receiving constellation according to the formula (1) in step 6.2)g；
8) Setting G to G +1, and if G > G, executing step 9); otherwise, turning to the step 5);
9) outputting G energy-lowest constellations in G iterations
Figure BDA0002180121910000058
As a final prediction result, G ∈ {1, 2.
The present embodiment takes protein 1FNA with sequence length of 91 as an example, and provides a multi-modal protein structure prediction method based on sequence niches, and the method comprises the following steps:
1) inputting sequence information of a target protein;
2) acquiring fragment library files of 3 fragments and 9 fragments from a ROBETTA server (http:// www.robetta.org /) according to a target protein sequence;
3) setting parameters: the maximum iteration time G is 5, the energy function coefficient k is 1, and the degradation function coefficient m is 0.001;
4) setting G ═ 1, G ∈ {1, 2.., G };
5) and (3) conformation initialization: generating an initial constellation using the first and second phases of the Rosetta protocol
Figure BDA0002180121910000059
If g is 1, continue with step 6); otherwiseGo to step 7);
6) the initial modal conformation generation operation is as follows:
6.1) recording P as the target conformation to
Figure BDA00021801219100000510
Running the fourth phase of the Rosetta protocol as the initial constellation and setting the energy function M in Rosettag(P) score3(P), note
Figure BDA00021801219100000511
The receiving constellation with the highest energy in the fourth phase of Rosetta
Figure BDA00021801219100000512
Figure BDA00021801219100000513
The lowest energy receiving conformation;
6.2) notes
Figure BDA00021801219100000514
Respectively, the lowest energy receiving conformation
Figure BDA00021801219100000515
The dihedral angle of the ith residue of (1),
Figure BDA00021801219100000516
respectively, the highest energy receiving conformation
Figure BDA00021801219100000517
L is the length of the sequence of the target protein, and the radius of niche r is calculated as followsg：
Figure BDA0002180121910000054
6.3) performing step 8);
7) the multimodal conformation generation procedure is as follows:
7.1) The label P is the target conformation,
Figure BDA0002180121910000067
φi、ωidihedral angles, M, of the i-th residue of the target conformation P, respectivelyg(P) is the energy function of the g-th iteration,
Figure BDA0002180121910000064
rg-1respectively the highest energy value, the lowest energy conformation and the niche radius of the g-1 iteration,
Figure BDA0002180121910000065
is the distance between the target conformation and the energy-minimum conformation, to
Figure BDA0002180121910000066
The fourth stage of the Rosetta protocol was performed as the initial constellation and the energy function was calculated as follows:
Figure BDA0002180121910000061
Figure BDA0002180121910000062
Figure BDA0002180121910000063
7.2) notesThe receiving constellation with the highest energy in the fourth phase of Rosetta
Figure BDA0002180121910000069
Figure BDA00021801219100000610
Calculating the niche radius r for the lowest energy receiving constellation according to the formula (1) in step 6.2)g；
8) Setting G to G +1, and if G > G, executing step 9); otherwise, turning to the step 5);
9) outputting G energy-lowest constellations in G iterations
Figure BDA00021801219100000611
As a final prediction result, G ∈ {1, 2.
Using protein 1FNA with sequence length of 91 as an example, the above method is used to obtain the near-natural state conformation of the protein, the conformation renewal scheme is shown in FIG. 1, and the root mean square deviation between the 5 structures obtained after 5 runs and the natural state structure is respectively
Figure BDA00021801219100000612
The predicted three-dimensional structure is shown in fig. 2.
While the foregoing illustrates one embodiment of the invention showing advantageous results, it will be apparent that the invention is not limited to the above-described embodiment, but is capable of numerous modifications without departing from the basic inventive concepts and without exceeding the scope of the inventive concepts.

Claims (1)

1. A multi-modal protein structure prediction method based on sequence niches is characterized in that: the method comprises the following steps:
1) inputting sequence information of a target protein;
2) acquiring fragment library files of 3 fragments and 9 fragments from a ROBETTA server according to a target protein sequence;
3) setting parameters: maximum iteration times G, an energy function coefficient k and a degradation function coefficient m;
4) setting G ═ 1, G ∈ {1, 2.., G };
5) and (3) conformation initialization: generating an initial constellation using the first and second phases of the Rosetta protocol
Figure FDA0002180121900000011
If g is 1, continue with step 6); otherwise, go to step 7);
6) the initial modal conformation generation operation is as follows:
6.1) recording P as the target conformation to
Figure FDA0002180121900000012
Running the fourth phase of the Rosetta protocol as the initial constellation and setting the energy function M in Rosettag(P) score3(P), note
Figure FDA0002180121900000013
The receiving constellation with the highest energy in the fourth phase of Rosetta
Figure FDA0002180121900000014
The lowest energy receiving conformation;
6.2) notes
Figure FDA0002180121900000015
Respectively, the lowest energy receiving conformation
Figure FDA0002180121900000016
The dihedral angle of the ith residue of (1),
Figure FDA0002180121900000017
respectively, the highest energy receiving conformationL is the length of the sequence of the target protein, and the radius of niche r is calculated as followsg：
Figure FDA0002180121900000019
6.3) performing step 8);
7) the multimodal conformation generation procedure is as follows:
7.1) noting P as the target conformation,
Figure FDA00021801219000000110
φi、ωidihedral angles, M, of the i-th residue of the target conformation P, respectivelyg(P) is the energy function of the g-th iteration,
Figure FDA00021801219000000111
rg-1respectively the highest energy value, the lowest energy conformation and the niche radius of the g-1 iteration,is the distance between the target conformation and the energy-minimum conformation, toThe fourth stage of the Rosetta protocol was performed as the initial constellation and the energy function was calculated as follows:
Figure FDA00021801219000000114
Figure FDA0002180121900000021
Figure FDA0002180121900000022
7.2) notes
Figure FDA0002180121900000023
The receiving constellation with the highest energy in the fourth phase of Rosetta
Figure FDA0002180121900000024
Calculating the niche radius r for the lowest energy receiving constellation according to the formula (1) in step 6.2)g；
8) Setting G to G +1, and if G > G, executing step 9); otherwise, turning to the step 5);
9) output ofG energy-lowest conformations in G iterations
Figure FDA0002180121900000025
As a final prediction result, G ∈ {1, 2.
CN201910793341.1A 2019-08-27 2019-08-27 Multi-modal protein structure prediction method based on sequence niche Active CN110706741B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910793341.1A CN110706741B (en) 2019-08-27 2019-08-27 Multi-modal protein structure prediction method based on sequence niche

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910793341.1A CN110706741B (en) 2019-08-27 2019-08-27 Multi-modal protein structure prediction method based on sequence niche

Publications (2)

Publication Number Publication Date
CN110706741A true CN110706741A (en) 2020-01-17
CN110706741B CN110706741B (en) 2021-08-03

Family

ID=69193655

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910793341.1A Active CN110706741B (en) 2019-08-27 2019-08-27 Multi-modal protein structure prediction method based on sequence niche

Country Status (1)

Country Link
CN (1) CN110706741B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106503485A (en) * 2016-09-23 2017-03-15 浙江工业大学 A kind of multi-modal differential evolution protein structure ab initio prediction method of local enhancement
CN107506613A (en) * 2017-08-29 2017-12-22 浙江工业大学 A kind of multi-modal protein conformation space optimization method based on multiple structural features
CN108804868A (en) * 2018-03-30 2018-11-13 浙江工业大学 A kind of protein two benches conformational space optimization method based on dihedral angle entropy
CN109448784A (en) * 2018-08-29 2019-03-08 浙江工业大学 A kind of Advances in protein structure prediction based on the selection of dihedral angle information auxiliary energy function

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106503485A (en) * 2016-09-23 2017-03-15 浙江工业大学 A kind of multi-modal differential evolution protein structure ab initio prediction method of local enhancement
CN107506613A (en) * 2017-08-29 2017-12-22 浙江工业大学 A kind of multi-modal protein conformation space optimization method based on multiple structural features
CN108804868A (en) * 2018-03-30 2018-11-13 浙江工业大学 A kind of protein two benches conformational space optimization method based on dihedral angle entropy
CN109448784A (en) * 2018-08-29 2019-03-08 浙江工业大学 A kind of Advances in protein structure prediction based on the selection of dihedral angle information auxiliary energy function

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张贵军 等: ""动态小生境半径两阶段多模态差分进化算法"", 《控制与决策》 *

Also Published As

Publication number Publication date
CN110706741B (en) 2021-08-03

Similar Documents

Publication Publication Date Title
Yang et al. A new size‐independent score for pairwise protein structure alignment and its application to structure classification and nucleic‐acid binding prediction
Berger et al. Computational solutions for omics data
CN108846256B (en) Group protein structure prediction method based on residue contact information
Bernt et al. Bioinformatics methods for the comparative analysis of metazoan mitochondrial genome sequences
CN109033744B (en) Protein structure prediction method based on residue distance and contact information
CN109872770B (en) Variable strategy protein structure prediction method combined with displacement degree evaluation
CN109360601B (en) Multi-modal protein structure prediction method based on displacement strategy
CN111180005B (en) Multi-modal protein structure prediction method based on niche resampling
CN110706741B (en) Multi-modal protein structure prediction method based on sequence niche
CN108920894B (en) Protein conformation space optimization method based on brief abstract convex estimation
CN109346128B (en) Protein structure prediction method based on residue information dynamic selection strategy
He et al. Protein complexes identification with family-wise error rate control
CN109461470B (en) Protein structure prediction energy function weight optimization method
CN109243526B (en) Protein structure prediction method based on specific fragment crossing
CN109326321B (en) Abstract convex estimation-based k-nearest neighbor protein structure prediction method
CN111815036B (en) Protein structure prediction method based on multi-residue contact map cooperative constraint
CN109411013B (en) Group protein structure prediction method based on individual specific variation strategy
CN109390035B (en) Protein conformation space optimization method based on local structure comparison
CN109461471B (en) Adaptive protein structure prediction method based on championship mechanism
CN110634531B (en) Protein structure prediction method based on double-layer bias search
Wang et al. Reconstruction of Protein Backbone with the alpha-Carbon Coordinates.
CN111161791B (en) Experimental data-assisted adaptive strategy protein structure prediction method
CN112085244B (en) Multi-target optimized protein structure prediction method based on residue contact diagram
CN108563921B (en) Protein structure prediction algorithm evaluation index construction method
KR101479735B1 (en) sequence likelihood ratio measurement system using Fast Global Alignmer algorith and sequence likelihood ratio measurement system using the same

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20200117

Assignee: ZHEJIANG ORIENT GENE BIOTECH CO.,LTD.

Assignor: JIANG University OF TECHNOLOGY

Contract record no.: X2023980053610

Denomination of invention: A multimodal protein structure prediction method based on sequence niche

Granted publication date: 20210803

License type: Common License

Record date: 20231222

EE01 Entry into force of recordation of patent licensing contract