Fast and accurate prediction of protein side-chain conformations

Summary: We developed a fast and accurate side-chain modeling program [Optimized Side Chain Atomic eneRgy (OSCAR)-star] based on orientation-dependent energy functions and a rigid rotamer model. The average computing time was 18 s per protein for 218 test proteins with higher prediction accuracy (1.1% increase for χ1 and 0.8% increase for χ1+2) than the best performing program developed by other groups. We show that the energy functions, which were calibrated to tolerate the discrete errors of rigid rotamers, are appropriate for protein loop selection, especially for decoys without extensive structural refinement

Computational design with flexible backbone sampling for protein remodeling and scaffolding of complex binding sites

Dissertation presented to obtain the Doutoramento (Ph.D.) degree in Biochemistry at the Instituto de Tecnologia Qu mica e Biol ogica da Universidade Nova de LisboaComputational protein design has achieved several milestones, including the design of a new protein fold, the design of enzymes for reactions that lack natural catalysts, and the re-engineering of protein-protein and protein-DNA binding speci city. These achievements have spurred demand to apply protein design methods to a wider array of research problems. However, the existing computational methods have largely relied on xed-backbone approaches that may limit the scope of problems that can be tackled. Here, we describe four computational protocols - side chain grafting, exible backbone remodeling, backbone grafting, and de novo sca old design - that expand the methodological protein design repertoire, three of which incorporate backbone exibility. Brie y, in the side chain grafting method, side chains of a structural motif are transplanted to a protein with a similar backbone conformation; in exible backbone remodeling, de novo segments of backbone are built and designed; in backbone grafting, structural motifs are explicitly grafted onto other proteins; and in de novo sca olding, a protein is folded and designed around a structural motif. We developed these new methods for the design of epitope-sca old vaccines in which viral neutralization epitopes of known three-dimensional structure were transplanted onto nonviral sca old proteins for conformational stabilization and immune presentation.(...

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

Strategies for Computational Protein Design with Application to the Development of a Biomolecular Tool-kit for Single Molecule Protein Sequencing

One of the key properties of proteins is that they exhibit remarkable affinities and specificities for small-molecule and peptide binding partners. To improve the success rate of rational, computational protein design and widen the scope of potential applications, it is useful to define generalized strategies and automated methodology to improve and/or alter the affinity and specificity of interactions. I have implemented several strategies for engineering protein-small molecule interactions including: improvement of substrate accessibility, stabilization of the bound state, truncation and surface engineering, and transplantation of residue level, native (or native-like) interactions. Each strategy was applied to one or more model protein, and the resulting changes in affinity, specificity, and activity were characterized experimentally. Finally, we designed a biomolecular tool-kit, consisting of 17 engineered proteins for amino acid side-chain recognition and a single enzyme to catalyze the Edman degradation. We profiled the affinity and specificity of each protein, and implemented a computational framework that demonstrates its utility for amino acid calling in a single molecule protein sequencing assay

Characterising side-chain motions in proteins by Nuclear Magnetic Resonance and Molecular Dynamics to report on function and regulation

Analysing the motions proteins undergo is vital for understanding a wide variety of biological processes. In particular side chains provide a wide range of chemical groups allowing proteins to carry out diverse functions such as catalysis and regulating gene expression. A key theme in this thesis is understanding the roles side-chains play in protein dynamics. To do this we use molecular dynamics, density functional theory and nuclear magnetic resonance. The first part of this work describes the relationship between the isoleucine side- chain conformation and chemical shift. We show there is a clear dependence between the χ angles and the observed side-chain’s 13C chemical shifts. This relationship is then used to determine rotamer distributions in the L24A FF domain’s excited state and the 42 kDa membrane complex DsbA-DsbB. In addition we use our methodology to show that the isoleucine random coil distribution in two model peptides is substantially different to the statistical distribution derived from the PDB. The second part of this thesis focuses on characterising the dynamic processes reg- ulating histone deacetylase 8. Here two approaches are used. The first concentrates on molecular dynamics to show the allosteric connection between the active site, the bind- ing rail and I19, a naturally occurring mutation site in patients. In conjunction with this we aimed to carry out a backbone independent methyl assignment. To aid joining intra- residue methyls we developed the HMBC-HMQC that utilises scalar coupling based transfers. This has many advantages over NOE based approaches as it directly reports on the bonding network, greatly simplifying the interpretation of crowded regions of the spectra. In addition to this we also made substantial progress towards assigning the ILV methyls by determining the residue types, joining intra-residue methyls and building an NOE network between the observed resonances

H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing

Accurately modeling protein 3D structure is essential for the design of functional proteins. An important sub-task of structure modeling is protein side-chain packing: predicting the conformation of side-chains (rotamers) given the protein's backbone structure and amino-acid sequence. Conventional approaches for this task rely on expensive sampling procedures over hand-crafted energy functions and rotamer libraries. Recently, several deep learning methods have been developed to tackle the problem in a data-driven way, albeit with vastly different formulations (from image-to-image translation to directly predicting atomic coordinates). Here, we frame the problem as a joint regression over the side-chains' true degrees of freedom: the dihedral $\chi$ angles. We carefully study possible objective functions for this task, while accounting for the underlying symmetries of the task. We propose Holographic Packer (H-Packer), a novel two-stage algorithm for side-chain packing built on top of two light-weight rotationally equivariant neural networks. We evaluate our method on CASP13 and CASP14 targets. H-Packer is computationally efficient and shows favorable performance against conventional physics-based algorithms and is competitive against alternative deep learning solutions.Comment: Accepted as a conference paper at MLCB 2023. 8 pages main body, 20 pages with appendix. 10 figure

Thermal adaptation of conformational dynamics in ribonuclease H

Structural changes are critical to the ability of proteins, particularly enzymes, to carry out their biological function. However, flexibility also leaves proteins vulnerable to denaturation and degradation; thus a balance must be struck between the dynamics required for function and the rigidity required for maintaining a globular protein's characteristic folded structure. These relationships have been studied in detail through comparison of homologous proteins from organisms adapted to varying properties of the bulk environment. In particular, organisms adapted to temperature extremes offer fruitful platforms for the investigation of adaptive changes in protein stability as a function of environmental pressures. Thermostable proteins are widely reported to be more rigid than their homologs from mesophilic organisms, and those from psychrophiles more flexible; this suggests the possibility of evolutionary conservation of the balance between dynamics and stability. Thus specifically functional aspects of protein dynamics may be isolable through the comparative analysis of members of protein families from organisms adapted to different thermal environments. The best experimental tool for characterizing internal conformational dynamics of proteins on a range of timescales and at site-specific resolution is nuclear magnetic resonance (NMR) spectroscopy, which has found widespread use in the study of protein flexibility and dynamics. However, it is often difficult to provide a detailed structural interpretation of NMR observations. This gap can be bridged using molecular dynamics (MD) simulations, which can directly simulate motional processes that have been observed experimentally. The potential for deep synergy between these two complementary tools has been recognized since MD methods were first applied to biological macromolecules, and recent technological developments have reinforced the mutually beneficial relationship between the two techniques. Ribonuclease HI (RNase H), an 18 kD globular protein that hydrolyzes the RNA strand of RNA:DNA hybrid substrates, has been extensively studied by NMR to characterize the differences in dynamics between homologs from the mesophilic organism \textit{E. coli} and the thermophilic organism \textit{T. thermophilus}. However, these dynamic differences are subtle and difficult to interpret structurally. The series of studies described in the present work was conceived in the pursuit of an improved understanding of the complex relationships between protein dynamics, activity, and thermostability in the RNase H protein family. The organizing principle of the work presented herein has been the close coupling between molecular dynamics simulations and NMR observations, permitting both validation of the MD trajectories by rigorous comparison to experiment and improved interpretation of the dynamics observed by NMR. Previous NMR observations of E. coli and T. thermophilus are integrated into an interpretive framework derived from simulations of the larger RNase H family. First, comparative analysis of molecular dynamics simulations of a total of five homologous RNase H families from organisms of varying preferred growth temperature reveals systematic differences in the conformational dynamics of the handle region, a loop previously identified as contributing to substrate binding. Second, analysis of the effects of activating mutations on the dynamics of ttRNH identifies rotamer dynamics whose contributions to increased catalytic activity can be rationalized in the context of observed differences in sidechain orientation in the wild-type ecRNH and ttRNH simulations. Third, a combined MD-NMR study finds that the active site residues of ecRNH, and likely of the entire RNase H family, are rigid on the ps-ns timescale while undergoing substantial conformational exchange upon Mg2+ binding; this suggests that the active site is electrostatically preorganized for binding the first metal ion, which in turn induces dynamic reorganization at longer timescales. Finally, long-timescale simulations of the RNase H family, despite unexpected local unfolding for some family members, identify handle-loop and rotamer preferences for the C. tepidum RNase H (ctRNH) homolog that unexpectedly differ from those observed for ecRNH and ttRNH, and which can be experimentally tested by NMR spectroscopy of this recently characterized and less well-studied example of an RNase H homolog from a thermophilic organism

Rotamer-specific Statistical Potentials for Protein Structure Modeling.

Knowledge-based (or statistical) potentials are widely used as essential tools in protein structure modeling and quality assessment. They are derived from experimentally determined protein structures aiming to extract relevant structural features that characterize the tightly folded structures. Since the surrounding circumstances are inhomogeneous and anisotropic, multibody contributions are important for accurate account of cooperative effects of molecular interactions. On the other hand, protein residues have great flexibility. It is energetically favorable for residues to adopt only a limited number of staggered conformations, known as rotamers. Depending on the rotameric state, the residue conformation and intra-residue interaction vary significantly within protein structures, resulting in different solvent accessibility and different electric polarization effect as well as different steric effect on residue elements. The major goal of this thesis is the design and development of statistical potentials that take into account the rotamer-dependence of interactions. We hypothesized that the rotameric state of residues is related to the specificity of interactions within protein structures. We first investigated how amino acid residues in PDB structures show different interaction patterns with the environment depending on their rotameric states. Observed rotamer-specific environmental features were incorporated to a scoring function, ProtGrid for protein designs. Our tests demonstrated that the ProtGrid is superior to widely used Rosetta energy function in prediction of the native amino acid types and rotameric states. Next, we formulated a rotamer-specific atomic statistical potential, named ROTAS that extends an existing orientation-dependent atomic potential (GOAP) by including the influence of rotameric states of residues on the specificity of interactions. The results showed that ROTAS performs better than other competing potentials not only in native structure recognition, but also in best model selection and correlation coefficients between energy and model quality. Finally, we applied the ROTAS potential to the problem of side-chain prediction. Compared with the existing side-chain modeling programs, ROTAS achieved comparable or even better prediction accuracy. We expect that the effectiveness of our energy functions would provide insightful information for the development of many applications which require accurate side-chain modeling such as homology modeling, protein design, mutation analysis, protein-protein docking and flexible ligand docking.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102342/1/jungkap_1.pd

Loop Prediction and Homology Modeling with High Resolution

Three-dimensional (3D) structure of a protein is essential as the guidance of structure-based drug dis-covery. To achieve robust homology modeling with atomic-level accuracy, reliable loop predictions are required. Here, a novel hierarchical protocol of Protein Local Optimization Program (PLOP) is designed to produce sub-2 angstrom predictions on loop regions in homology modeling. Dramatic improvements in both speed and accuracy have been realized with implementation of special-designed clustering and adaptive loop closure algorithm. Four prediction rounds are designed for homology modeling as the high-level protocol of PLOP, which allows latter rounds employ the educated guess of backbone atom positions and hydrogen bonding information inherited from the previous rounds, contributing to additional prediction accuracy. The success of PLOP has been demonstrated with four different data sets, mainly concen-trating on homology modeling of H3 loops of antibodies. GPU-accelerated sampling algorithm and deep learning models are implemented, which are able to produce promising predictions as input templates for PLOP in the context of homology modeling