Induced fit with replica exchange improves protein complex structure prediction.
Induced fit with replica exchange improves protein complex structure prediction.
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DOI：
10.1371/journal.pcbi.1010124
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发表时间：
2022-06
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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尽管在过去十年中蛋白质复合物的预测取得了进展，但最近的盲蛋白质复合物结构预测挑战揭示了在结合后表现出显著构象变化的靶标上的有限成功率（DockQ得分> 0.4的模型小于20%）。为了克服捕获骨干运动的限制，我们开发了一种新的，积极的采样方法，结合温度副本交换蒙特卡罗（T-REMC）和构象采样技术在罗塞塔对接协议。我们的方法，ReplicaDock 2.0，模拟诱导适合机制的蛋白质结合到样品骨架运动在假定的接口残基上的飞行，从而概括结合伴侣诱导的构象变化。此外，ReplicaDock 2.0的时钟在150-500 CPU小时每个目标（蛋白质大小的依赖）;运行时间明显快于基于分子动力学的方法。对于一组88种具有中度至高度柔性的蛋白质（未结合到结合的iRMSD超过1.2 μ m），ReplicaDock 2.0成功对接了61%的中度柔性复合物和35%的高度柔性复合物。此外，我们证明，通过使骨架采样特别偏向于包含柔性环或铰链结构域的残基，可以预测高度柔性的靶标的准确度低于2 μ m。这表明，当移动的蛋白质片段是已知的，额外的收益是可能的。蛋白质以高度特异性和受调控的方式相互结合，这些相关的结合动力学与它们的功能密切相关。传统的结构测定技术，如冷冻电镜，X射线晶体学和核磁共振是费时费力。使用模拟“诱导拟合”结合的动力学机制的温度-副本交换蒙特卡罗方法，我们改进了蛋白质复合物结构的预测，特别是对于在结合时表现出相当大的构象变化的靶标（界面均方根偏差（未结合-结合）> 1.2 μ m）。捕获这些结合诱导的蛋白质构象变化可以帮助我们更好地理解生物学机制，并为疾病机制提出干预策略。
Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Å), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 Å accuracy. This indicates that additional gains are possible when mobile protein segments are known. Proteins bind each other in a highly specific and regulated manner, and these associated dynamics of binding are intimately linked to their function. Conventional techniques of structure determination such as cryo-EM, X-ray crystallography and NMR are time-consuming and arduous. Using a temperature-replica exchange Monte Carlo approach that mimics the kinetic mechanism of “induced fit” binding, we improved prediction of protein complex structures, particularly for targets that exhibit considerable conformational changes upon binding (Interface root mean square deviation (unbound-bound) > 1.2 Å. Capturing these binding-induced conformational changes in proteins can aid us in better understanding biological mechanisms and suggest intervention strategies for disease mechanisms.
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影响因子：
14.8
作者：
Kozakov D;Hall DR;Xia B;Porter KA;Padhorny D;Yueh C;Beglov D;Vajda S
通讯作者：
Vajda S
影响因子：
2.9
作者：
Lensink MF;Brysbaert G;Mauri T;Nadzirin N;Velankar S;Chaleil RAG;Clarence T;Bates PA;Kong R;Liu B;Yang G;Liu M;Shi H;Lu X;Chang S;Roy RS;Quadir F;Liu J;Cheng J;Antoniak A;Czaplewski C;Giełdoń A;Kogut M;Lipska AG;Liwo A;Lubecka EA;Maszota-Zieleniak M;Sieradzan AK;Ślusarz R;Wesołowski PA;Zięba K;Del Carpio Muñoz CA;Ichiishi E;Harmalkar A;Gray JJ;Bonvin AMJJ;Ambrosetti F;Vargas Honorato R;Jandova Z;Jiménez-García B;Koukos PI;Van Keulen S;Van Noort CW;Réau M;Roel-Touris J;Kotelnikov S;Padhorny D;Porter KA;Alekseenko A;Ignatov M;Desta I;Ashizawa R;Sun Z;Ghani U;Hashemi N;Vajda S;Kozakov D;Rosell M;Rodríguez-Lumbreras LA;Fernandez-Recio J;Karczynska A;Grudinin S;Yan Y;Li H;Lin P;Huang SY;Christoffer C;Terashi G;Verburgt J;Sarkar D;Aderinwale T;Wang X;Kihara D;Nakamura T;Hanazono Y;Gowthaman R;Guest JD;Yin R;Taherzadeh G;Pierce BG;Barradas-Bautista D;Cao Z;Cavallo L;Oliva R;Sun Y;Zhu S;Shen Y;Park T;Woo H;Yang J;Kwon S;Won J;Seok C;Kiyota Y;Kobayashi S;Harada Y;Takeda-Shitaka M;Kundrotas PJ;Singh A;Vakser IA;Dapkūnas J;Olechnovič K;Venclovas Č;Duan R;Qiu L;Xu X;Zhang S;Zou X;Wodak SJ
通讯作者：
Wodak SJ
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：
3.7
作者：
Chaudhury S;Berrondo M;Weitzner BD;Muthu P;Bergman H;Gray JJ
通讯作者：
Gray JJ
影响因子：
6.8
作者：
Harmalkar A;Gray JJ
通讯作者：
Gray JJ