The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

Designing tight binding ligands is a primary objective of small molecule drug discovery.Over the past few decades, free energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low cost parallel computing.However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (~5X in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations.Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized based on other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.3

National and local initiatives focused on the transformation of STEM teaching in higher education have multiplied over the last decade. These initiatives often focus on measuring change in instructional practices, but it is difficult to monitor such change without a national picture of STEM educational practices, especially as characterized by common observational instruments. We characterized a snapshot of this landscape by conducting the first large scale observation-based study. We found that lecturing was prominent throughout the undergraduate STEM curriculum, even in classrooms with infrastructure designed to support active learning, indicating that further work is required to reform STEM education. Additionally, we established that STEM faculty’s instructional practices can vary substantially within a course, invalidating the commonly-used teaching evaluations based on a one-time observation.

We carried out an experiment to determine whether student learning gains in a large, traditionally taught, upper-division lecture course in developmental biology could be increased by partially changing to a more interactive classroom format. In two successive semesters, we presented the same course syllabus using different teaching styles: in fall 2003, the traditional lecture format; and in spring 2004, decreased lecturing and addition of student participation and cooperative problem solving during class time, including frequent in-class assessment of understanding. We used performance on pretests and posttests, and on homework problems to estimate and compare student learning gains between the two semesters. Our results indicated significantly higher learning gains and better conceptual understanding in the more interactive course. To assess reproducibility of these effects, we repeated the interactive course in spring 2005 with similar results. Our findings parallel results of similar teaching-style comparisons made in other disciplines. On the basis of this evidence, we propose a general model for teaching large biology courses that incorporates interactive engagement and cooperative work in place of some lecturing, while retaining course content by demanding greater student responsibility for learning outside of class.

When students answer an in-class conceptual question individually using clickers, discuss it with their neighbors, and then revote on the same question, the percentage of correct answers typically increases. This outcome could result from gains in understanding during discussion, or simply from peer influence of knowledgeable students on their neighbors. To distinguish between these alternatives in an undergraduate genetics course, we followed the above exercise with a second, similar (isomorphic) question on the same concept that students answered individually. Our results indicate that peer discussion enhances understanding, even when none of the students in a discussion group originally knows the correct answer.

Using single-molecule fluorescence resonance energy transfer, we have defined bacterial RNA polymerase (RNAP) clamp formation at each step in transcription initiation and elongation. We find that the clamp predominantly is open in free RNAP and early intermediates in transcription initiation, but closes upon formation of a catalytically competent transcription initiation complex and remains closed during initial transcription and transcription elongation. We show that four RNAP inhibitors interfere with clamp opening. We propose that clamp opening allows DNA to be loaded into and unwound in the RNAP active-center cleft, that DNA loading and unwinding trigger clamp closure, and that clamp closure accounts for the high stability of initiation complexes and the high stability and processivity of elongation complexes.

Accurate and reliable calculation of protein-ligand binding affinities remains a hotbed of computer-aided drug design research. Despite the potentially large impact FEP (free energy perturbation) may have in drug design projects, practical applications of FEP in industrial contexts have been limited. In this work, we use a recently developed method, FEP/REST (free energy perturbation/replica exchange with solute tempering), to calculate the relative binding affinities for a set of congeneric ligands binding to the CDK2 receptor. We compare the FEP/REST results with traditional FEP/MD (molecular dynamics) results and MM/GBSA (molecular mechanics/Generalized Born Surface Area model) results and examine why FEP/REST performed notably better than these other methods, as well as why certain ligand mutations lead to large increases of the binding affinity while others do not. We also introduce a mathematical framework for assessing the consistency and reliability of the calculations using cycle closures in FEP mutation paths.

We have used systematic fluorescence resonance energy transfer and distance-constrained docking to define the three-dimensional structures of bacterial RNA polymerase holoenzyme and the bacterial RNA polymerase-promoter open complex in solution. The structures provide a framework for understanding sigma(70)-(RNA polymerase core), sigma(70)-DNA, and sigma(70)-RNA interactions. The positions of sigma(70) regions 1.2, 2, 3, and 4 are similar in holoenzyme and open complex. In contrast, the position of sigma(70) region 1.1 differs dramatically in holoenzyme and open complex. In holoenzyme, region 1.1 is located within the active-center cleft, apparently serving as a "molecular mimic" of DNA, but, in open complex, region 1.1 is located outside the active center cleft. The approach described here should be applicable to the analysis of other nanometer-scale complexes.