Performance of Web tools for predicting changes in protein stability caused by mutations.
Performance of Web tools for predicting changes in protein stability caused by mutations.
复制标题
预测由突变引起的蛋白质稳定性变化的Web工具的性能。
DOI：
10.1186/s12859-021-04238-w
复制
发表时间：
2021-07-05
影响因子：
3
通讯作者：
Facchiano A
中科院分区：
文献类型：
--
作者：
Marabotti A;Del Prete E;Scafuri B;Facchiano A
尽管开发了几十年的专用Web工具，但仍然很难正确预测突变引起的蛋白质热力学稳定性的变化。在这里，我们评估了五个最近开发的Web工具的可靠性，以评估该领域的进展。结果表明，虽然在该领域有所改进，但评估的预测因子仍远未达到理想水平。普遍存在的问题包括偏向于不稳定突变，一般来说，当突变导致ΔΔG在± 0.5 kcal/mol范围内时，结果不可靠。我们发现，使用几个预测因子并将其结果结合成共识是一种粗略但有效的方法，可以提高预测的可靠性。我们建议所有开发人员在他们未来的工具中考虑使用平衡数据集来训练预测器，所有用户联合收割机将多个工具的结果结合起来，以增加正确预测突变对蛋白质热力学稳定性影响的机会。在线版本包含补充材料，可通过10.1186/s12859-021-04238-w获得。
Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ΔΔG within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein. The online version contains supplementary material available at 10.1186/s12859-021-04238-w.
登录
查看更多内容
影响因子：
14.9
作者：
Sillitoe I;Bordin N;Dawson N;Waman VP;Ashford P;Scholes HM;Pang CSM;Woodridge L;Rauer C;Sen N;Abbasian M;Le Cornu S;Lam SD;Berka K;Varekova IH;Svobodova R;Lees J;Orengo CA
通讯作者：
Orengo CA
DOI：
10.1016/j.str.2017.10.009
发表时间：
2017-12-05
期刊：
Structure (London, England : 1993)
影响因子：
--
作者：
Gore S;Sanz García E;Hendrickx PMS;Gutmanas A;Westbrook JD;Yang H;Feng Z;Baskaran K;Berrisford JM;Hudson BP;Ikegawa Y;Kobayashi N;Lawson CL;Mading S;Mak L;Mukhopadhyay A;Oldfield TJ;Patwardhan A;Peisach E;Sahni G;Sekharan MR;Sen S;Shao C;Smart OS;Ulrich EL;Yamashita R;Quesada M;Young JY;Nakamura H;Markley JL;Berman HM;Burley SK;Velankar S;Kleywegt GJ
通讯作者：
Kleywegt GJ
DOI：
10.1007/978-1-4939-8736-8_1
发表时间：
2019-01-01
期刊：
COMPUTATIONAL METHODS IN PROTEIN EVOLUTION
影响因子：
--
作者：
Strokach, Alexey;Corbi-Verge, Carles;Kim, Philip M.
通讯作者：
Kim, Philip M.
影响因子：
5.8
作者：
Pucci, Fabrizio;Bernaerts, Katrien, V;Rooman, Marianne
通讯作者：
Rooman, Marianne
影响因子：
14.9
作者：
Burley SK;Bhikadiya C;Bi C;Bittrich S;Chen L;Crichlow GV;Christie CH;Dalenberg K;Di Costanzo L;Duarte JM;Dutta S;Feng Z;Ganesan S;Goodsell DS;Ghosh S;Green RK;Guranović V;Guzenko D;Hudson BP;Lawson CL;Liang Y;Lowe R;Namkoong H;Peisach E;Persikova I;Randle C;Rose A;Rose Y;Sali A;Segura J;Sekharan M;Shao C;Tao YP;Voigt M;Westbrook JD;Young JY;Zardecki C;Zhuravleva M
通讯作者：
Zhuravleva M