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The Integrated Graduate Program in Physical and Engineering Biology (IGPPEB) at Yale University brings together Ph.D. students from the physical, engineering, and biological sciences. The main goals of this program are for students to become comfortable working in an interdisciplinary and collaborative research environment and adept at communicating with scientists and nonscientists. To fill a student‐identified learning gap in engaging in inclusive discussions, IGPPEB students developed a communication workshop to improve skills in visual engagement, citing specific content, constructive conversation entrances, and encouragement of peers. Based on short‐ and long‐term assessment of the workshop, 100% of students reported that it should be offered to future cohorts and 63% of students perceived it to be personally helpful. Additionally, 92% of participants reported using one or more of the core skills beyond the course, with skills in "Encouraging peers" and "Constructive conversation entrances" rated the highest in perceived improvement. Based on the highest average rating of 76 ± 24 (on a scale of 0–100), students agreed that the workshop made them feel more welcome in the IGPPEB community. With a rating of 68 ± 13, they also agreed that the workshop had a positive impact on their graduate school experience. Participants provided suggestions for future improvements, such as increasing student involvement in leading discussions of course material. This study demonstrates that a student‐led workshop can improve perceived discussion skills and build community across an interdisciplinary program in the sciences. [ABSTRACT FROM AUTHOR]

Numerous studies have investigated the differences and similarities between protein structures determined by solution NMR spectroscopy and those determined by X‐ray crystallography. A fundamental question is whether any observed differences are due to differing methodologies or to differences in the behavior of proteins in solution versus in the crystalline state. Here, we compare the properties of the hydrophobic cores of high‐resolution protein crystal structures and those in NMR structures, determined using increasing numbers and types of restraints. Prior studies have reported that many NMR structures have denser cores compared with those of high‐resolution X‐ray crystal structures. Our current work investigates this result in more detail and finds that these NMR structures tend to violate basic features of protein stereochemistry, such as small non‐bonded atomic overlaps and few Ramachandran and sidechain dihedral angle outliers. We find that NMR structures solved with more restraints, and which do not significantly violate stereochemistry, have hydrophobic cores that have a similar size and packing fraction as their counterparts determined by X‐ray crystallography at high resolution. These results lead us to conclude that, at least regarding the core packing properties, high‐quality structures determined by NMR and X‐ray crystallography are the same, and the differences reported earlier are most likely a consequence of methodology, rather than fundamental differences between the protein in the two different environments. [ABSTRACT FROM AUTHOR]

There have been several studies suggesting that protein structures solved by NMR spectroscopy and X‐ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high‐quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures—in the root‐mean‐square deviations of the Cα atomic positions, identities of core amino acids, backbone, and side‐chain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modeling protein cores as jammed packings of amino acid‐shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by X‐ray crystallography. In contrast, highly thermalized packing‐generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and X‐ray crystallography. [ABSTRACT FROM AUTHOR]

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute significantly to their stability. We used two sources to obtain datasets of decoys to compare with real protein structures: submissions to the biennial Critical Assessment of protein Structure Prediction competition, in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence, and also decoys generated by 3DRobot, which have user‐specified global root‐mean‐squared deviations from experimentally determined structures. Our analysis revealed that both sets of decoys possess cores that do not recapitulate the key features that define real protein cores. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a feed‐forward neural network, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoy structures with similar accuracy to that obtained by state‐of‐the‐art methods that incorporate many additional features. The small number of physical features makes our model interpretable, emphasizing the importance of protein packing and hydrophobicity in protein structure prediction. [ABSTRACT FROM AUTHOR]

We present the structure of an engineered protein–protein interface between two beta barrel proteins, which is mediated by interactions between threonine (Thr) residues. This Thr zipper structure suggests that the protein interface is stabilized by close‐packing of the Thr residues, with only one intermonomer hydrogen bond (H‐bond) between two of the Thr residues. This Thr‐rich interface provides a unique opportunity to study the behavior of Thr in the context of many other Thr residues. In previous work, we have shown that the side chain (χ1) dihedral angles of interface and core Thr residues can be predicted with high accuracy using a hard sphere plus stereochemical constraint (HS) model. Here, we demonstrate that in the Thr‐rich local environment of the Thr zipper structure, we are able to predict the χ1 dihedral angles of most of the Thr residues. Some, however, are not well predicted by the HS model. We therefore employed explicitly solvated molecular dynamics (MD) simulations to further investigate the side chain conformations of these residues. The MD simulations illustrate the role that transient H‐bonding to water, in combination with steric constraints, plays in determining the behavior of these Thr side chains. Broader Audience Statement: Protein–protein interactions are critical to life and the search for ways to disrupt adverse protein–protein interactions involved in disease is an ongoing area of drug discovery. We must better understand protein–protein interfaces, both to be able to disrupt existing ones and to engineer new ones for a variety of biotechnological applications. We have discovered and characterized an artificial Thr‐rich protein–protein interface. This novel interface demonstrates a heretofore unknown property of Thr‐rich surfaces: mediating protein–protein interactions. PDB Code(s): 6CIU [ABSTRACT FROM AUTHOR]

In 2008, we established the Integrated Graduate Program in Physical and Engineering Biology (IGPPEB) at Yale University. Our goal was to create a comprehensive graduate program to train a new generation of scientists who possess a sophisticated understanding of biology and who are capable of applying physical and quantitative methodologies to solve biological problems. Here we describe the framework of the training program, report on its effectiveness, and also share the insights we gained during its development and implementation. The program features co-teaching by faculty with complementary specializations, student peer learning, and novel hands-on courses that facilitate the seamless blending of interdisciplinary research and teaching. It also incorporates enrichment activities to improve communication skills, engage students in science outreach, and foster a cohesive program cohort, all of which promote the development of transferable skills applicable in a variety of careers. The curriculum of the graduate program is integrated with the curricular requirements of several Ph.D.-granting home programs in the physical, engineering, and biological sciences. Moreover, the wide-ranging recruiting activities of the IGPPEB serve to enhance the quality and diversity of students entering graduate school at Yale. We also discuss some of the challenges we encountered in establishing and optimizing the program, and describe the institution-level changes that were catalyzed by the introduction of the new graduate program. The goal of this article is to serve as both an inspiration and as a practical 'how to' manual for those who seek to establish similar programs at their own institutions. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(6):537-549, 2016. [ABSTRACT FROM AUTHOR]

ABSTRACT Methionine (Met) is a structurally versatile amino acid most commonly found in protein cores and at protein-protein interfaces. Thus, a complete description of the structure of Met is important for a fundamental understanding of protein structure and design. In previous work, we showed that the hard-sphere dipeptide model is able to recapitulate the side-chain dihedral angle distributions observed in high-resolution protein crystal structures for the nine amino acids we have studied to date: Val, Thr, Ser, Leu, Ile, Cys, Tyr, Trp, and Phe. Using the same approach, we are also able to predict the observed χ1 and χ2 side-chain dihedral angle distributions for Met. However, the form of the side-chain dihedral angle distribution P( χ3) predicted by the hard-sphere model does not match the observed distribution. We investigate the possible origins of the discrepancy and find that specific bond lengths and angles in Met side chains strongly influence P( χ3). We then identify minimal additions to the hard-sphere dipeptide model necessary to quantitatively predict P( χ3) of Met, and its near isosteres norleucine (Nle) and selenomethionine (Mse). We find that adding weak attractive interactions between hydrogen atoms to the model is sufficient to achieve predictions for P( χ3) that closely match the observed P( χ3) distributions for Met, Nle, and Mse. We explicitly show that weak attractive interactions between hydrogens do not negatively affect the agreement between the predicted and observed side-chain dihedral angle distribution for Val, Leu, Ile, and Phe, as we expect for other amino acids. Proteins 2016; 84:900-911. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT Despite recent improvements in computational methods for protein design, we still lack a quantitative, predictive understanding of the intrinsic probabilities for amino acids to adopt particular side-chain conformations. Surprisingly, this question has remained unsettled for many years, in part because of inconsistent results from different experimental approaches. To explicitly determine the relative populations of different side-chain dihedral angles, we performed all-atom hard-sphere Langevin Dynamics simulations of leucine (Leu) and isoleucine (Ile) dipeptide mimetics with stereo-chemical constraints and repulsive-only steric interactions between non-bonded atoms. We determine the relative populations of the different χ1 and χ2 dihedral angle combinations as a function of the backbone dihedral angles ϕ and ψ. We also propose, and test, a mechanism for inter-conversion between the different side-chain conformations. Specifically, we discover that some of the transitions between side-chain dihedral angle combinations are very frequent, whereas others are orders of magnitude less frequent, because they require rare coordinated motions to avoid steric clashes. For example, to transition between different values of χ2, the Leu side-chain bond angles κ1 and κ2 must increase, whereas to transition in χ1, the Ile bond angles λ1 and λ2 must increase. These results emphasize the importance of computational approaches in stimulating further experimental studies of the conformations of side-chains in proteins. Moreover, our studies emphasize the power of simple steric models to inform our understanding of protein structure, dynamics, and design. Proteins 2015; 83:1488-1499. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT Building on the pioneering work of Ho and DeGrado (J Am Chem Soc 1987, 109, 6751-6758) in the late 1980s, protein design approaches have revealed many fundamental features of protein structure and stability. We are now in the era that the early work presaged - the design of new proteins with practical applications and uses. Here we briefly survey some past milestones in protein design, in addition to highlighting recent progress and future aspirations. © 2015 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 104: 334-350, 2015. [ABSTRACT FROM AUTHOR]

The side-chain dihedral angle distributions of all amino acids have been measured from myriad high-resolution protein crystal structures. However, we do not yet know the dominant interactions that determine these distributions. Here, we explore to what extent the defining features of the side-chain dihedral angle distributions of different amino acids can be captured by a simple physical model. We find that a hard-sphere model for a dipeptide mimetic that includes only steric interactions plus stereochemical constraints is able to recapitulate the key features of the back-bone dependent observed amino acid side-chain dihedral angle distributions of Ser, Cys, Thr, Val, Ile, Leu, Phe, Tyr, and Trp. We find that for certain amino acids, performing the calculations with the amino acid of interest in the central position of a short α-helical segment improves the match between the predicted and observed distributions. We also identify the atomic interactions that give rise to the differences between the predicted distributions for the hard-sphere model of the dipeptide and that of the α-helical segment. Finally, we point out a case where the hard-sphere plus stereochemical constraint model is insufficient to recapitulate the observed side-chain dihedral angle distribution, namely the distribution P(χ3) for Met. Proteins 2014; 82:2574-2584. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

A fundamental question in protein science is what is the intrinsic propensity for an amino acid to be in an α-helix, β-sheet, or other backbone dihedral angle (ø-ψ) conformation. This question has been hotly debated for many years because including all protein crystal structures from the protein database, increases the probabilities for α-helical structures, while experiments on small peptides observe that β-sheet-like conformations predominate. We perform molecular dynamics (MD) simulations of a hard-sphere model for Ala dipeptide mimetics that includes steric interactions between nonbonded atoms and bond length and angle constraints with the goal of evaluating the role of steric interactions in determining protein backbone conformational preferences. We find four key results. For the hard-sphere MD simulations, we show that (1) β-sheet structures are roughly three and half times more probable than a-helical structures, (2) transitions between α-helix and β-sheet structures only occur when the backbone bond angle τ (N-Cα-C) is greater than 110°, and (3) the probability distribution of s for Ala conformations in the "bridge" region of ø-ψ space is shifted to larger angles compared to other regions. In contrast, (4) the distributions obtained from Amber and CHARMM MD simulations in the bridge regions are broader and have increased τ compared to those for hard sphere simulations and from high-resolution protein crystal structures. Our results emphasize the importance of hard-sphere interactions and local stereochemical constraints that yield strong correlations between ø-ψ conformations and τ. [ABSTRACT FROM AUTHOR]

The pioneering work of Ramachandran and colleagues emphasized the dominance of steric constraints in specifying the structure of polypeptides. The ubiquitous Ramachandran plot of backbone dihedral angles (ϕ and ψ) defined the allowed regions of conformational space. These predictions were subsequently confirmed in proteins of known structure. Ramachandran and colleagues also investigated the influence of the backbone angle τ on the distribution of allowed ϕ/ψ combinations. The 'bridge region' (ϕ ≤ 0° and −20° ≤ ψ ≤ 40°) was predicted to be particularly sensitive to the value of τ. Here we present an analysis of the distribution of ϕ/ψ angles in 850 non-homologous proteins whose structures are known to a resolution of 1.7 Å or less and sidechain B-factor less than 30 Å. We show that the distribution of ϕ/ψ angles for all 87,000 residues in these proteins shows the same dependence on τ as predicted by Ramachandran and colleagues. Our results are important because they make clear that steric constraints alone are sufficient to explain the backbone dihedral angle distributions observed in proteins. Contrary to recent suggestions, no additional energetic contributions, such as hydrogen bonding, need be invoked. [ABSTRACT FROM AUTHOR]