Rigorous assessment and integration of the sequence and structure based features to predict hot spots

Background Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. Results In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. Conclusion Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots

Rigorous assessment and integration of the sequence and structure based features to predict hot spots

<p>Abstract</p> <p>Background</p> <p>Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need.</p> <p>Results</p> <p>In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes.</p> <p>Conclusion</p> <p>Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.</p

Highly Efficient Polarized GeS/MoSe2 van der Waals Heterostructure for Water Splitting from Ultraviolet to Near‐Infrared Light

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152744/1/pssr201900582.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152744/2/pssr201900582_am.pd

Strain Enhanced Visible–Ultraviolet Absorption of Blue Phosphorene/MoX2 (X = S,Se) Heterolayers

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149231/1/pssr201800659.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149231/2/pssr201800659_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149231/3/pssr201800659-sup-0001-SuppFig-S1.pd

Pollen Viability, Pistil Receptivity, and Embryo Development in Hybridization of Nelumbo nucifera Gaertn

Seed set is usually low and differs for different crosses of flower lotus (Nelumbo nucifera Gaertn.). The reasons remain unknown, and this has a negative impact on lotus breeding. To determine the causes, we carried out two crosses of flower lotus, that is, “Jinsenianhua” × “Qinhuaihuadeng” and “Qinhuaihuadeng” × “Jinsenianhua” and pollen viability, pistil receptivity, and embryo development were investigated. The pollen grains collected at 05:00-06:00 hrs had the highest viability, and the viabilities of “Jinsenianhua” and “Qinhuaihuadeng” were 20.6 and 15.7%, respectively. At 4 h after artificial pollination, the number of pollen grains germinating on each stigma reached a peak: 63.0 and 17.2 per stigma in “Jinsenianhua” × “Qinhuaihuadeng” and “Qinhuaihuadeng” × “Jinsenianhua”, respectively. At 1 d after artificial pollination, the percentages of normal embryos in the two crosses were 55.0 and 21.9%, respectively; however, at 11 d after pollination, the corresponding percentages were 20.8 and 11.2%. Seed sets of the two crosses were 17.9 and 8.0%, respectively. The results suggested that low pistil receptivity and embryo abortion caused low seed set in “Qinhuaihuadeng” × “Jinsenianhua”, whereas low fecundity of “Jinsenianhua” × “Qinhuaihuadeng” was mainly attributable to embryo abortion

A Thorough Search for Short Timescale Periodicity in Five Repeating FRBs

Fast Radio Bursts (FRBs) are bright radio transients with millisecond durations which typically occur at extragalactic distances. The association of FRB 20200428 with the Galactic magnetar SGR J1935+2154 strongly indicates that they could originate from neutron stars, which naturally leads to the expectation that periodicity connected with the spinning of magnetars should exist in the activities of repeating FRBs. However, previous studies have failed to find any signatures supporting such a conjecture. Here we perform a thorough search for short timescale periodicity in the five most active repeating sources, i.e. FRBs 20121102A, 20180916B, 20190520B, 20200120E, and 20201124A. Three different methods are employed, including the phase folding algorithm, the Schuster periodogram and the Lomb-Scargle periodogram. For the two most active repeaters from which more than 1600 bursts have been detected, i.e. FRB 20121102A and FRB 20201124A, more in-depth period searches are conducted by considering various burst properties such as the pulse width, peak flux, fluence, and the brightness temperature. For these two repeaters, we have also selected those days on which a large number of bursts were detected and performed periodicity analysis based on the single-day bursts. No periodicity in a period range of 1 ms-1000 s is found in all the efforts, although possible existence of a very short period between 1 ms-10 ms still could not be completely excluded for FRBs 20200120E and 20201124A due to limited timing accuracy of currently available observations. Implications of such a null result on the theoretical models of FRBs are discussed.Comment: 22 pages, 23 figures, 1 tabl