Protein-Protein Docking with F2Dock 2.0 and GB-Rerank

Rezaul Chowdhury is with UT Austin; Muhibur Rasheed is with UT Austin; Maysam Moussalem is with UT Austin; Donald Keidel is with The Scripps Research Institute; Arthur Olson is with The Scripps Research Institute; Michel Sanner is with The Scripps Research Institute; Chandrajit Bajaj is with The Scripps Research Institute.Motivation -- Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F2 Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error. Results -- The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F2 Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F2 Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F2 Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F2 Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other. Availability -- The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/soft​ware/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/soft​ware/f2dockclient.shtml.The research of C.B., R.C., M.M., and M.R. of University of Texas, was supported in part by National Science Foundation (NSF) grant CNS-0540033, and grants from the National Institutes of Health (NIH) R01-GM074258, R01-GM073087, R01-EB004873. The research of M.M. was additionally supported by an NSF Graduate Research Fellowship. The research of M.S. and A.O. of TSRI was supported in part by a subcontract on NIH grant R01-GM073087. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Computer Science

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report

Comparison of the performance of F² Dock 2.0 and ZDock 3.0.2 for each of the 25 antibody-antigen and antigen-bound antibody complexes from ZLab’s benchmark 4.0 in terms of the rank and RMSD of the top hit and the best hit.

<p>Boldfaced entries indicate better performance on the particular metric for the complex. Empty entries indicate that no hits were found for that complex by the corresponding protocol.</p

Definition of skin and core for shape complementarity.

<p>(Left) Traditional <i>double skin-layer</i> approach for shape complementarity, (Right) Improved approach with curvature-based weighting of skin atoms and depth dependent weighting of core atoms of molecule , and depth dependent weighting of the atoms of .</p

Comparison of ZDock 3.0.2 [21] and F² Dock 2.0.

<p>(a) On all 176 complexes from Zlab Benchmark 4.0 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051307#pone.0051307-Hwang1" target="_blank">[37]</a>, (b) On 25 antibody-antigen and antigen-bound antibody complexes, (c) On 52 enzyme-inhibitor and enzyme-substrate complexes, and (d) on the 99 other type of complexes.</p

High-level overview of rigid-body protein-protein docking using F² Dock 2.0 and GB-rerank.

<p>F² Dock 2.0 performs exhaustive 6D search in discretized rotational and translational space where it computes a score for each sampled orientation of the ligand with respect to a stationary receptor. The scoring function is a weighted combination of shape complementarity, electrostatics and interface propensity based affinity terms (refer to Section 2.3 for details). The top few orientations (poses) of the ligand are kept in a priority queue. Then top several thousand poses from the queue are clustered based on the distance between the geometric centers of different poses of . All but the best scoring pose of a cluster is penalized by reducing the score. The resulting reordered list is then passed through several soft filters in order to further penalize potential false positives. Finally, as a separate post-processing step, the ranked docking poses are re-scored and reranked based on the change in solvation energy caused by each pose.</p

Comparison of the performance of F² Dock 2.0 and ZDock 3.0.2 for each of the 52 enzyme-inhibitor and enzyme-substrate complexes from ZLab’s benchmark 4.0 in terms of the rank and RMSD of the top hit and the best hit.

<p>Comparison of the performance of F² Dock 2.0 and ZDock 3.0.2 for each of the 52 enzyme-inhibitor and enzyme-substrate complexes from ZLab’s benchmark 4.0 in terms of the rank and RMSD of the top hit and the best hit.</p

Running time of F² Dock 2.0 and its components.

<p>(<b>a</b>) Average running time of each affinity function and filter of F² Dock 2.0. GB-rerank consumes a major portion of the time (42%), the FFT phase takes about 30% time and the rest is taken by the filters and clustering. The labels in the figure are actual time in minutes. (<b>b</b>) Running times of F² Dock 2.0 on the rigid-body test cases from Zlab benchmark 2.0 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051307#pone.0051307-Mintseris2" target="_blank">[36]</a> showing percentage of running time due to each affinity function and filter of F² Dock 2.0 for each complex.</p

Performance of F² Dock 2.0 with and without user-specified complex type.

<p>When complex type is not specified in the input, F² Dock 2.0’s performance does not change significantly. In most cases, it can automatically detect the complex-type and apply the correct set of parameters. Tests are based on rigid body cases from Zlab’s Protein-protein docking Benchmark 2.0.</p

Comparison of the performance of F² Dock 2.0 and ZDock 3.0.2 for each of the 99 other type of complexes from ZLab’s benchmark 4.0 in terms of the rank and RMSD of the top hit and the best hit.

<p>Comparison of the performance of F² Dock 2.0 and ZDock 3.0.2 for each of the 99 other type of complexes from ZLab’s benchmark 4.0 in terms of the rank and RMSD of the top hit and the best hit.</p