adabmDCA: adaptive Boltzmann machine learning for biological sequences.
adabmDCA: adaptive Boltzmann machine learning for biological sequences.
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AdabmDCA：生物序列的自适应Boltzmann机器学习。
DOI：
10.1186/s12859-021-04441-9
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发表时间：
2021-10-29
影响因子：
3
通讯作者：
Zamponi F
中科院分区：
文献类型：
--
作者：
Muntoni AP;Pagnani A;Weigt M;Zamponi F
Boltzmann机器是基于能量的模型，已被证明为进化相关蛋白质和RNA家族的结构域提供了准确的统计描述。它们是根据考虑残基保守的局部偏差和成对项来参数化的，以模拟残基之间的上位性共同进化。从模型参数中，可以提取对目标域的三维接触图的准确预测。最近，这些模型的准确性也根据它们预测突变效应和在电子功能序列中生成的能力进行了评估。我们的Boltzmann机器学习的自适应实现adabmDCA通常可以应用于蛋白质和RNA家族，并根据输入数据的复杂性和用户要求完成几个学习设置。该代码在https://github.com/anna-pa-m/adabmDCA.上完全可用作为例子，我们执行了三个Boltzmann机器的学习，模拟了ukitz和Beta-lacTamase2蛋白结构域以及TPP-核糖开关RNA结构域。AdabmDCA学习的模型在推断的联系人地图以及合成的序列的质量方面与这项任务的最新技术获得的模型相当。此外，该代码实现了平衡和非平衡学习，当平衡学习在计算时间方面令人望而却步时，允许进行准确和无损的训练，并允许使用基于信息的标准修剪不相关的参数。
Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are parametrized in terms of local biases accounting for residue conservation, and pairwise terms to model epistatic coevolution between residues. From the model parameters, it is possible to extract an accurate prediction of the three-dimensional contact map of the target domain. More recently, the accuracy of these models has been also assessed in terms of their ability in predicting mutational effects and generating in silico functional sequences. Our adaptive implementation of Boltzmann machine learning, adabmDCA, can be generally applied to both protein and RNA families and accomplishes several learning set-ups, depending on the complexity of the input data and on the user requirements. The code is fully available at https://github.com/anna-pa-m/adabmDCA. As an example, we have performed the learning of three Boltzmann machines modeling the Kunitz and Beta-lactamase2 protein domains and TPP-riboswitch RNA domain. The models learned by adabmDCA are comparable to those obtained by state-of-the-art techniques for this task, in terms of the quality of the inferred contact map as well as of the synthetically generated sequences. In addition, the code implements both equilibrium and out-of-equilibrium learning, which allows for an accurate and lossless training when the equilibrium one is prohibitive in terms of computational time, and allows for pruning irrelevant parameters using an information-based criterion.
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影响因子：
14.9
作者：
Mistry J;Chuguransky S;Williams L;Qureshi M;Salazar GA;Sonnhammer ELL;Tosatto SCE;Paladin L;Raj S;Richardson LJ;Finn RD;Bateman A
通讯作者：
Bateman A
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
32.4
作者：
Ferguson AL;Mann JK;Omarjee S;Ndung'u T;Walker BD;Chakraborty AK
通讯作者：
Chakraborty AK
影响因子：
6.3
作者：
Haldane, Allan;Levy, Ronald M.
通讯作者：
Levy, Ronald M.
影响因子：
2.4
作者：
Ekeberg, Magnus;Lovkvist, Cecilia;Aurell, Erik
通讯作者：
Aurell, Erik