Autofix for backward-fit sidechains: using MolProbity and real-space refinement to put misfits in their place

Misfit sidechains in protein crystal structures are a stumbling block in using those structures to direct further scientific inference. Problems due to surface disorder and poor electron density are very difficult to address, but a large class of systematic errors are quite common even in well-ordered regions, resulting in sidechains fit backwards into local density in predictable ways. The MolProbity web site is effective at diagnosing such errors, and can perform reliable automated correction of a few special cases such as 180° flips of Asn or Gln sidechain amides, using all-atom contacts and H-bond networks. However, most at-risk residues involve tetrahedral geometry, and their valid correction requires rigorous evaluation of sidechain movement and sometimes backbone shift. The current work extends the benefits of robust automated correction to more sidechain types. The Autofix method identifies candidate systematic, flipped-over errors in Leu, Thr, Val, and Arg using MolProbity quality statistics, proposes a corrected position using real-space refinement with rotamer selection in Coot, and accepts or rejects the correction based on improvement in MolProbity criteria and on χ angle change. Criteria are chosen conservatively, after examining many individual results, to ensure valid correction. To test this method, Autofix was run and analyzed for 945 representative PDB files and on the 50S ribosomal subunit of file 1YHQ. Over 40% of Leu, Val, and Thr outliers and 15% of Arg outliers were successfully corrected, resulting in a total of 3,679 corrected sidechains, or 4 per structure on average. Summary Sentences: A common class of misfit sidechains in protein crystal structures is due to systematic errors that place the sidechain backwards into the local electron density. A fully automated method called “Autofix” identifies such errors for Leu, Val, Thr, and Arg and corrects over one third of them, using MolProbity validation criteria and Coot real-space refinement of rotamers

Optimizing and evaluating protein microcrystallography experiments: strengths and weaknesses of X-rays and electrons

Recently, significant technological innovations have enabled the measurement of both X-ray and electron diffraction from protein microcrystals. These new microcrystallography experiments are useful when large crystals cannot be obtained, but also in other cases, such as when large crystals suffer from long-range disorder, or when uniform perturbations need to be applied rapidly to the entire crystal volume. Optimizing the preparation of protein microcrystals for this new class of experiments presents new challenges for crystallographers, who have traditionally sought to grow large, single crystals. To better understand these new challenges, we optimized the production of microcrystalline samples of cyclophilin A (CypA), starting from conditions that produced millimeter scale crystals. Next, we used these microcrystals to determine CypA structures by serial femtosecond crystallography (SFX) at two XFEL lightsources, and by microcrystal electron diffraction (microED) in an electron cryomicroscope. Here, I will present our optimization strategy for protein microcrystallization, and compare the results of X-ray and electron microcrystallography experiments with CypA. I will focus on the unique caveats of sample delivery for each method, and compare the resulting structures. The goal will be to provide insight into which microcrystallography experiment is most appropriate for which types of samples, and to share our experience with sample preparation and delivery for each type of experiment

A development of assistant surgical robot system based on surgical-operation-by-wire and hands-on-throttle-and-stick

BACKGROUND: Robot-assisted laparoscopic surgery offers several advantages compared with open surgery and conventional minimally invasive surgery. However, one issue that needs to be resolved is a collision between the robot arm and the assistant instrument. This is mostly caused by miscommunication between the surgeon and the assistant. To resolve this limitation, an assistant surgical robot system that can be simultaneously manipulated via a wireless controller is proposed to allow the surgeon to control the assistant instrument. METHODS: The system comprises two novel master interfaces (NMIs), a surgical instrument with a gripper actuated by a micromotor, and 6-axis robot arm. Two NMIs are attached to master tool manipulators of da Vinci research kit (dVRK) to control the proposed system simultaneously with patient side manipulators of dVRK. The developments of the surgical instrument and NMI are based on surgical-operation-by-wire concept and hands-on-throttle-and-stick concept from the earlier research, respectively. Tests for checking the accuracy, latency, and power consumption of the NMI are performed. The gripping force, reaction time, and durability are assessed to validate the surgical instrument. The workspace is calculated for estimating the clinical applicability. A simple peg task using the fundamentals of laparoscopic surgery board and an in vitro test are executed with three novice volunteers. RESULTS: The NMI was operated for 185 min and reflected the surgeon’s decision successfully with a mean latency of 132 ms. The gripping force of the surgical instrument was comparable to that of conventional systems and was consistent even after 1000 times of gripping motion. The reaction time was 0.4 s. The workspace was calculated to be 8397.4 cm(3). Recruited volunteers were able to execute the simple peg task within the cut-off time and successfully performed the in vitro test without any collision. CONCLUSIONS: Various experiments were conducted and it is verified that the proposed assistant surgical robot system enables collision-free and simultaneous operation of the dVRK’s robot arm and the proposed assistant robot arm. The workspace is appropriate for the performance of various kinds of surgeries. Therefore, the proposed system is expected to provide higher safety and effectiveness for the current surgical robot system

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Modeling of Hidden Structures Using Sparse Chemical Shift Data from NMR Relaxation Dispersion

NMR relaxation dispersion measurements report on conformational changes occurring on the μs-ms timescale. Chemical shift information derived from relaxation dispersion can be used to generate structural models of weakly populated alternative conformational states. Current methods to obtain such models rely on determining the signs of chemical shift changes between the conformational states, which are difficult to obtain in many situations. Here, we use a "sample and select" method to generate relevant structural models of alternative conformations of the C-terminal-associated region of Escherichia coli dihydrofolate reductase (DHFR), using only unsigned chemical shift changes for backbone amides and carbonyls (1H, 15N, and 13C'). We find that CS-Rosetta sampling with unsigned chemical shift changes generates a diversity of structures that are sufficient to characterize a minor conformational state of the C-terminal region of DHFR. The excited state differs from the ground state by a change in secondary structure, consistent with previous predictions from chemical shift hypersurfaces and validated by the x-ray structure of a partially humanized mutant of E. coli DHFR (N23PP/G51PEKN). The results demonstrate that the combination of fragment modeling with sparse chemical shift data can determine the structure of an alternative conformation of DHFR sampled on the μs-ms timescale. Such methods will be useful for characterizing alternative states, which can potentially be used for in silico drug screening, as well as contributing to understanding the role of minor states in biology and molecular evolution

Design of a Minimally Invasive Surgical Teleoperated Master-Slave System with Haptic Feedback

Conventional Minimally Invasive Surgery (MIS)is performed with long and slender camera and instruments through at least three small incisions. However, it generally provides the surgeon with an uncomfortable body posture, limited force feedback and unnatural eye-hand-coordination. A masterslave system with force feedback is being developed, since sucha system can overcome the inconveniences of MIS. This paper is about the design of the master and the slave