scholarly journals RaptorX-Property: a web server for protein structure property prediction

2016 ◽  
Vol 44 (W1) ◽  
pp. W430-W435 ◽  
Author(s):  
Sheng Wang ◽  
Wei Li ◽  
Shiwang Liu ◽  
Jinbo Xu
2010 ◽  
Vol 38 (Web Server) ◽  
pp. W569-W575 ◽  
Author(s):  
F. Lauck ◽  
C. A. Smith ◽  
G. F. Friedland ◽  
E. L. Humphris ◽  
T. Kortemme

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lupeng Kong ◽  
Fusong Ju ◽  
Haicang Zhang ◽  
Shiwei Sun ◽  
Dongbo Bu

Abstract Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions.


1998 ◽  
Vol 279 (1) ◽  
pp. 30-38 ◽  
Author(s):  
C.L. Philip Chen ◽  
Yang Cao ◽  
Steven R. LeClair

2012 ◽  
Vol 40 (W1) ◽  
pp. W323-W328 ◽  
Author(s):  
J. P. G. L. M. Rodrigues ◽  
M. Levitt ◽  
G. Chopra

2019 ◽  
Vol 20 (S19) ◽  
Author(s):  
Lei Deng ◽  
Guolun Zhong ◽  
Chenzhe Liu ◽  
Judong Luo ◽  
Hui Liu

Abstract Background Protein comparative analysis and similarity searches play essential roles in structural bioinformatics. A couple of algorithms for protein structure alignments have been developed in recent years. However, facing the rapid growth of protein structure data, improving overall comparison performance and running efficiency with massive sequences is still challenging. Results Here, we propose MADOKA, an ultra-fast approach for massive structural neighbor searching using a novel two-phase algorithm. Initially, we apply a fast alignment between pairwise structures. Then, we employ a score to select pairs with more similarity to carry out a more accurate fragment-based residue-level alignment. MADOKA performs about 6–100 times faster than existing methods, including TM-align and SAL, in massive alignments. Moreover, the quality of structural alignment of MADOKA is better than the existing algorithms in terms of TM-score and number of aligned residues. We also develop a web server to search structural neighbors in PDB database (About 360,000 protein chains in total), as well as additional features such as 3D structure alignment visualization. The MADOKA web server is freely available at: http://madoka.denglab.org/ Conclusions MADOKA is an efficient approach to search for protein structure similarity. In addition, we provide a parallel implementation of MADOKA which exploits massive power of multi-core CPUs.


1983 ◽  
Vol 4 ◽  
pp. 305
Author(s):  
E. M. Schulson ◽  
J. H. Currier

Structure/property relationships, while well-researched in metallic and in some ceramic materials, have been essentially ignored 1n studies on the mechanical properties of ice. To rectify this situation, experiments have been designed and have been underway for the past two years to investigate one mechanical property, i.e. tensile strength, and the effect of one structural feature, i.e. grain size, on this property. A clear relationship has been established, and is reported here. Other work is in progress and will also be reported in due course. Equiaxed and randomly oriented aggregates of freshwater ice, of grain size (as seen in two-dimensional sections) varying from 1.0 to 7.3 mm, were prepared in the form of large cylinders (91 mm diameter × 231 mm length). The aggregates were deformed to fracture under uniaxial tension, using a specially designed ball-joint and yoke assembly to ensure axial loading. Data were obtained at -10 ±0.2°C (i.e. at 96% of the melting point) at a strain-rate of 10−6 s−1. Figure 1 shows that the tensile strength decreases with increasing grain size, from 1.25 MPa for d = 1 mm to 0.80 MPa for d = 7 mm. Moreover, this figure illustrates that the data are highly reproducible; i.e. that strength is reproducible to within ±5% for a given grain size over the complete range. Fig. 1. Graph showing the decrease in the tensile strength of ice with increasing grain size. Concerning the functional relationship between tensile strength of and grain size, analysis shows that the following equation is well obeyed: Where σj is 0.6 MPa and k is 0.02 MPa m½ at -10°C and 10−6 s−1. This point is illustrated in Figure 2. Fig. 2. Hall-Petch plot showing the relationship between the tensile strength and the grain size of the ice. The d−½ character of this relationship, which is of the classical Hall-Petch form observed frequently in metallic materials, indicates that the tensile strength of ice is controlled by some process involving stress concentration, possibly the propagation of microcracks nucleated by the interactions of dislocations or the propagation of pre-existing defects. Of these, the former is the more probable. The reason is that processes involving dislocation motion, when expressed by the difference of σf – σi, are expected to increase linearly with increasing d−½, whereas processes involving the propagation of pre-existing defects predict a linear relationship between σf and d−½ which extrapolates through the origin. The former behavior is the one observed. It is thus concluded: (i) that the tensile strength of equiaxed and randomly oriented freshwater ice, when deformed slowly at -10°C, decreases with increasing grain size, (ii) that the functional relationship between tensile strength σf and grain size d is σf = σj + kd−½, where σj and k are materials parameters, and (iii) that the tensile strength of polycrystalline ice is controlled by the propagation in a brittle manner of microcracks nucleated by dislocation interactions. Acknowledgement This work was funded by the US Army Research Office, Contract No. DAA G-29-80-C-0064.


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