Biomedical subjects

Matthew P Jacobson

Publications and source records attributed to Matthew P Jacobson.

At least 19 recordsLinked to original sources

PubMed2007

Prediction of side-chain conformations on protein surfaces.

An approach is described that improves the prediction of the conformations of surface side chains in crystal structures, given the main-chain conformation of a protein. A key element of the methodology involves the use of the colony energy. This phenomenological term favors conformations found in frequently sampled regions, thereby approximating entropic effects and serving to smooth the potential energy surface. Use of the colony energy significantly improves prediction accuracy for surface side chains with little additional computational cost. Prediction accuracy was quantified as the percentage of side-chain dihedral angles predicted to be within 40 degrees of the angles measured by X-ray diffraction. Use of the colony energy in predictions for single side chains improved the prediction accuracy for chi(1) and chi(1+2) from 65 and 40% to 74 and 59%, respectively. Several other factors that affect prediction of surface side-chain conformations were also analyzed, including the extent of conformational sampling, details of the rotamer library employed, and accounting for the crystallographic environment. The prediction of conformations for polar residues on the surface was generally found to be more difficult than those for hydrophobic residues, except for polar residues participating in hydrogen bonds with other protein groups. For surface residues with hydrogen-bonded side chains, the prediction accuracy of chi(1) and chi(1+2) was 79 and 63%, respectively. For surface polar residues, in general (all side-chain prediction), the accuracy of chi(1) and chi(1+2) was only 73 and 56%, respectively. The most accurate results were obtained using the colony energy and an all-atom description that includes neighboring molecules in the crystal (protein chains and hetero atoms). Here, the accuracy of chi(1) and chi(1+2) predictions for surface side chains was 82 and 73%, respectively. The root mean square deviations obtained for hydrogen-bonding surface side chains were 1.64 and 1.81 A, with and without consideration of crystal packing effects, respectively.

Crystallography, X-Ray↗

PubMed2007

Assignment of polar states for protein amino acid residues using an interaction cluster decomposition algorithm and its application to high resolution protein structure modeling.

We have developed a new method (Independent Cluster Decomposition Algorithm, ICDA) for creating all-atom models of proteins given the heavy-atom coordinates, provided by X-ray crystallography, and the pH. In our method the ionization states of titratable residues, the crystallographic mis-assignment of amide orientations in Asn/Gln, and the orientations of OH/SH groups are addressed under the unified framework of polar states assignment. To address the large number of combinatorial possibilities for the polar hydrogen states of the protein, we have devised a novel algorithm to decompose the system into independent interacting clusters, based on the observation of the crucial interdependence between the short range hydrogen bonding network and polar residue states, thus significantly reducing the computational complexity of the problem and making our algorithm tractable using relatively modest computational resources. We utilize an all atom protein force field (OPLS) and a Generalized Born continuum solvation model, in contrast to the various empirical force fields adopted in most previous studies. We have compared our prediction results with a few well-documented methods in the literature (WHATIF, REDUCE). In addition, as a preliminary attempt to couple our polar state assignment method with real structure predictions, we further validate our method using single side chain prediction, which has been demonstrated to be an effective way of validating structure prediction methods without incurring sampling problems. Comparisons of single side chain prediction results after the application of our polar state prediction method with previous results with default polar state assignments indicate a significant improvement in the single side chain predictions for polar residues.

Algorithms↗

PubMed2006

Conformational flexibility, internal hydrogen bonding, and passive membrane permeability: successful in silico prediction of the relative permeabilities of cyclic peptides.

We report an atomistic physical model for the passive membrane permeability of cyclic peptides. The computational modeling was performed in advance of the experiments and did not involve the use of "training data". The model explicitly treats the conformational flexibility of the peptides by extensive conformational sampling in low (membrane) and high (water) dielectric environments. The passive membrane permeabilities of 11 cyclic peptides were obtained experimentally using a parallel artificial membrane permeability assay (PAMPA) and showed a linear correlation with the computational results with R(2) = 0.96. In general, the results support the hypothesis, already well established in the literature, that the ability to form internal hydrogen bonds is critical for passive membrane permeability and can be the distinguishing factor among closely related compounds, such as those studied here. However, we have found that the number of internal hydrogen bonds that can form in the membrane and the solvent-exposed polar surface area correlate more poorly with PAMPA permeability than our model, which quantitatively estimates the solvation free energy losses upon moving from high-dielectric water to the low-dielectric interior of a membrane.

Amino Acid Sequence↗

PubMed2006

Molecular mechanics methods for predicting protein-ligand binding.

Ligand binding affinity prediction is one of the most important applications of computational chemistry. However, accurately ranking compounds with respect to their estimated binding affinities to a biomolecular target remains highly challenging. We provide an overview of recent work using molecular mechanics energy functions to address this challenge. We briefly review methods that use molecular dynamics and Monte Carlo simulations to predict absolute and relative ligand binding free energies, as well as our own work in which we have developed a physics-based scoring method that can be applied to hundreds of thousands of compounds by invoking a number of simplifying approximations. In our previous studies, we have demonstrated that our scoring method is a promising approach for improving the discrimination between ligands that are known to bind and those that are presumed not to, in virtual screening of large compound databases. In new results presented here, we explore several improvements to our computational method including modifying the dielectric constant used for the protein and ligand interiors, and empirically scaling energy terms to compensate for deficiencies in the energy model. Future directions for further improving our physics-based scoring method are also discussed.

Algorithms↗

PubMed2006

Evolution of structure and function in the o-succinylbenzoate synthase/N-acylamino acid racemase family of the enolase superfamily.

Understanding how proteins evolve to provide both exquisite specificity and proficient activity is a fundamental problem in biology that has implications for protein function prediction and protein engineering. To study this problem, we analyzed the evolution of structure and function in the o-succinylbenzoate synthase/N-acylamino acid racemase (OSBS/NAAAR) family, part of the mechanistically diverse enolase superfamily. Although all characterized members of the family catalyze the OSBS reaction, this family is extraordinarily divergent, with some members sharing <15% identity. In addition, a member of this family, Amycolatopsis OSBS/NAAAR, is promiscuous, catalyzing both dehydration and racemization. Although the OSBS/NAAAR family appears to have a single evolutionary origin, no sequence or structural motifs unique to this family could be identified; all residues conserved in the family are also found in enolase superfamily members that have different functions. Based on their species distribution, several uncharacterized proteins similar to Amycolatopsis OSBS/NAAAR appear to have been transmitted by lateral gene transfer. Like Amycolatopsis OSBS/NAAAR, these might have additional or alternative functions to OSBS because many are from organisms lacking the pathway in which OSBS is an intermediate. In addition to functional differences, the OSBS/NAAAR family exhibits surprising structural variations, including large differences in orientation between the two domains. These results offer several insights into protein evolution. First, orthologous proteins can exhibit significant structural variation, and specificity can be maintained with little conservation of ligand-contacting residues. Second, the discovery of a set of proteins similar to Amycolatopsis OSBS/NAAAR supports the hypothesis that new protein functions evolve through promiscuous intermediates. Finally, a combination of evolutionary, structural, and sequence analyses identified characteristics that might prime proteins, such as Amycolatopsis OSBS/NAAAR, for the evolution of new activities.

Actinobacteria↗

PubMed2006

Conformational changes in protein loops and helices induced by post-translational phosphorylation.

Post-translational phosphorylation is a ubiquitous mechanism for modulating protein activity and protein-protein interactions. In this work, we examine how phosphorylation can modulate the conformation of a protein by changing the energy landscape. We present a molecular mechanics method in which we phosphorylate proteins in silico and then predict how the conformation of the protein will change in response to phosphorylation. We apply this method to a test set comprised of proteins with both phosphorylated and non-phosphorylated crystal structures, and demonstrate that it is possible to predict localized phosphorylation-induced conformational changes, or the absence of conformational changes, with near-atomic accuracy in most cases. Examples of proteins used for testing our methods include kinases and prokaryotic response regulators. Through a detailed case study of cyclin-dependent kinase 2, we also illustrate how the computational methods can be used to provide new understanding of how phosphorylation drives conformational change, why substituting Glu or Asp for a phosphorylated amino acid does not always mimic the effects of phosphorylation, and how a phosphatase can "capture" a phosphorylated amino acid. This work illustrates how computational methods can be used to elucidate principles and mechanisms of post-translational phosphorylation, which can ultimately help to bridge the gap between the number of known sites of phosphorylation and the number of structures of phosphorylated proteins.

Algorithms↗

PubMed2006

Testing the conformational hypothesis of passive membrane permeability using synthetic cyclic peptide diastereomers.

Little is known about the effect of conformation on passive membrane diffusion rates in small molecules. Evidence suggests that intramolecular hydrogen bonding may play a role by reducing the energetic cost of desolvating hydrogen bond donors, especially amide N-H groups. We set out to test this hypothesis by investigating the passive membrane diffusion characteristics of a series of cyclic peptide diastereomers based on the sequence cyclo[Leu-Leu-Leu-Leu-Pro-Tyr]. We identified two cyclic hexapeptide diastereomers based on this sequence, whose membrane diffusion rates differed by nearly two log units. Results of solution NMR studies and hydrogen/deuterium (H/D) exchange experiments showed that membrane diffusion rates correlated with the degree of intramolecular hydrogen bonding and H/D exchange rates. The most permeable diastereomer, cyclo[d-Leu-d-Leu-Leu-d-Leu-Pro-Tyr] (1), exhibited a passive membrane diffusion rate comparable to that of the orally available drug cyclosporine A.

Cell Membrane Permeability↗

PubMed2006

Novel human lipoxygenase inhibitors discovered using virtual screening with homology models.

We report the discovery of new, low micromolar, small molecule inhibitors of human platelet-type 12- and reticulocyte 15-lipoxygenase-1 (12-hLO and 15-hLO) using structure-based methods. Specifically, we created homology models of 12-hLO and 15-hLO, based on the structure of rabbit 15-lipoxygenase, for in silico screening of a large compound library followed by in vitro screening of 20 top scoring molecules. Eight of these compounds inhibited either 12- or 15-human lipoxygenase with lower than 100 microM affinity. Of these, we obtained IC50 values for the three best inhibitors, all of which displayed low micromolar inhibition. One compound showed specificity for 15-hLO versus 12-hLO; however, a selective inhibitor for 12-hLO was not identified. As a control we screened 20 randomly selected compounds, of which none showed low micromolar inhibition. The new low-micromolar inhibitors appear to be suitable as leads for further inhibitor development efforts against 12-hLO and 15-hLO, based on the fact their size and chemical properties are appropriate to classify them as drug-like compounds. The models of these protein-inhibitor complexes suggest strategies for future development of selective lipoxygenase inhibitors.

Animals↗

PubMed2006

Novel procedure for modeling ligand/receptor induced fit effects.

We present a novel protein-ligand docking method that accurately accounts for both ligand and receptor flexibility by iteratively combining rigid receptor docking (Glide) with protein structure prediction (Prime) techniques. While traditional rigid-receptor docking methods are useful when the receptor structure does not change substantially upon ligand binding, success is limited when the protein must be "induced" into the correct binding conformation for a given ligand. We provide an in-depth description of our novel methodology and present results for 21 pharmaceutically relevant examples. Traditional rigid-receptor docking for these 21 cases yields an average RMSD of 5.5 A. The average ligand RMSD for docking to a flexible receptor for the 21 pairs is 1.4 A; the RMSD is < or =1.8 A for 18 of the cases. For the three cases with RMSDs greater than 1.8 A, the core of the ligand is properly docked and all key protein/ligand interactions are captured.

Binding Sites↗

PubMed2006

What role do surfaces play in GB models? A new-generation of surface-generalized born model based on a novel gaussian surface for biomolecules.

We have developed a version of our surface generalized Born (SGB) model that employs a Gaussian surface, as opposed to the van der Waals surface used previously. The Gaussian surface is smooth and its properties are analytically differentiable with respect to the positions of atoms. A significant advantage of a solvent model based on this analytically differentiable surface is the availability of analytical gradients of the surface and solvation forces. An efficient and robust algorithm is designed to construct and triangulate the Gaussian surface for large biomolecules with arbitrary shapes, and to compute the various terms required for energy gradients. The Gaussian surface is shown to better mimic the boundary between the solute and solvent by properly addressing solvent accessibility, as is demonstrated by comparisons with standard Poisson-Boltzmann calculations for proteins of different sizes. These results also demonstrate that surface definition is a dominant contribution to differences between GB and PB calculations, especially if the system is large. Application of the new surface to prediction of long loop regions is presented, and significant improvement in the energetics is seen compared with results obtained using the van der Waals surface, even in the absence of optimized empirical correction terms that were used in the latter calculations.

Algorithms↗

PubMed2005

Surfaces affect ion pairing.

In water, positive ions attract negative ions. That attraction can be modulated if a hydrophobic surface is present near the two ions in water. Using computer simulations with explicit and implicit water, we study how an ion embedded on a hydrophobic surface interacts with another nearby ion in water. Using hydrophobic surfaces with different curvatures, we find that the contact interaction between a positive and negative ion is strongly affected by the curvature of an adjacent surface, either stabilizing or destabilizing the ion pair. We also find that the solvent-separated ion pair (SSIP) can be made more stable than the contacting ion pair by the presence of a surface. This may account for why bridging waters are often found in protein crystal structures. We also note that implicit solvent models do not account for SSIPs. Finally, we find that there are charge asymmetries: an embedded positive charge attracting a negative ion is different than an embedded negative charge attracting a positive ion. Such asymmetries are also not predicted by implicit solvent models. These results may be useful for improving computational models of solvation in biology and chemistry.

Computer Simulation↗

PubMed2005

Virtual ligand screening against Escherichia coli dihydrofolate reductase: improving docking enrichment using physics-based methods.

Motivated by their participation in the McMaster Data-Mining and Docking Competition, the authors developed 2 new computational technologies and applied them to docking against Escherichia coli dihydrofolate reductase: a receptor preparation procedure that incorporates rotamer optimization of side chains and a physics-based rescoring procedure for estimating relative binding affinities of the protein-ligand complexes. Both methods use the same energy function, consisting of the all-atom OPLS-AA force field and a generalized Born solvent model, which treats the protein receptor and small-molecule ligands in a consistent manner. Thus, the energy function is similar to that used in more sophisticated approaches, such as free-energy perturbation and the molecular mechanics Poisson-Boltzmann/surface area, but sampling during the rescoring procedure is limited to simple energy minimization of the ligand. The use of a highly efficient minimization algorithm permitted the authors to apply this rescoring procedure to hundreds of thousands of protein-ligand complexes during the competition, using a modest Linux cluster. To test these methods, they used the 12 competitive inhibitors identified in the training set, plus methotrexate, as positive controls in enrichment studies with both the training and test sets, each containing 50,000 compounds. The key conclusion is that combining the receptor preparation and rescoring methods makes it possible to identify most of the positive controls within the top few tenths of a percent of the rank-ordered training and test set libraries.

Computational Biology↗

PubMed2005

Virtual screening against highly charged active sites: identifying substrates of alpha-beta barrel enzymes.

We have developed a virtual ligand screening method designed to help assign enzymatic function for alpha-beta barrel proteins. We dock a library of approximately 19,000 known metabolites against the active site and attempt to identify the relevant substrate based on predicted relative binding free energies. These energies are computed using a physics-based energy function based on an all-atom force field (OPLS-AA) and a generalized Born implicit solvent model. We evaluate the ability of this method to identify the known substrates of several members of the enolase superfamily of enzymes, including both holo and apo structures (11 total). The active sites of these enzymes contain numerous charged groups (lysines, carboxylates, histidines, and one or more metal ions) and thus provide a challenge for most docking scoring functions, which treat electrostatics and solvation in a highly approximate manner. Using the physics-based scoring procedure, the known substrate is ranked within the top 6% of the database in all cases, and in 8 of 11 cases, it is ranked within the top 1%. Moreover, the top-ranked ligands are strongly enriched in compounds with high chemical similarity to the substrate (e.g., different substitution patterns on a similar scaffold). These results suggest that our method can be used, in conjunction with other information including genomic context and known metabolic pathways, to suggest possible substrates or classes of substrates for experimental testing. More broadly, the physics-based scoring method performs well on highly charged binding sites and is likely to be useful in inhibitor docking against polar binding sites as well. The method is fast (<1 min per ligand), due largely to an efficient minimization algorithm based on the truncated Newton method, and thus, it can be applied to thousands of ligands within a few hours on a small Linux cluster.

Alanine Racemase↗

PubMed2004

Tryptophan 500 and arginine 707 define product and substrate active site binding in soybean lipoxygenase-1.

There is much debate whether the fatty acid substrate of lipoxygenase binds "carboxylate-end first" or "methyl-end first" in the active site of soybean lipoxygenase-1 (sLO-1). To address this issue, we investigated the sLO-1 mutants Trp500Leu, Trp500Phe, Lys260Leu, and Arg707Leu with steady-state and stopped-flow kinetics. Our data indicate that the substrates (linoleic acid (LA), arachidonic acid (AA)), and the products (13-(S)-hydroperoxy-9,11-(Z,E)-octadecadienoic acid (HPOD) and 15-(S)-hydroperoxyeicosatetraeonic acid (15-(S)-HPETE)) interact with the aromatic residue Trp500 (possibly pi-pi interaction) and with the positively charged amino acid residue Arg707 (charge-charge interaction). Residue Lys260 of soybean lipoxygenase-1 had little effect on either the activation or steady-state kinetics, indicating that both the substrates and products bind "carboxylate-end first" with sLO-1 and not "methyl-end first" as has been proposed for human 15-lipoxygenase.

Arginine↗

PubMed2004

A hierarchical approach to all-atom protein loop prediction.

The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone root-mean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 A for 5 residue loops, 1.00/0.44 A for 8 residue loops, and 2.47/1.83 A for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 A for 5 residue loops, 0.84 A for 8 residue loops, and 1.63 A for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.

Algorithms↗

PubMed2004

High-resolution prediction of protein helix positions and orientations.

We have developed a new method for predicting helix positions in globular proteins that is intended primarily for comparative modeling and other applications where high precision is required. Unlike helix packing algorithms designed for ab initio folding, we assume that knowledge is available about the qualitative placement of all helices. However, even among homologous proteins, the corresponding helices can demonstrate substantial differences in positions and orientations, and for this reason, improperly positioned helices can contribute significantly to the overall backbone root-mean-square deviation (RMSD) of comparative models. A helix packing algorithm for use in comparative modeling must obtain high precision to be useful, and for this reason we utilize an all-atom protein force field (OPLS) and a Generalized Born continuum solvent model. To reduce the computational expense associated with using a detailed, physics-based energy function, we have developed new hierarchical and multiscale algorithms for sampling the helices and flanking loops. We validate the method using a test suite of 33 cases, which are drawn from a diverse set of high-resolution crystal structures. The helix positions are reproduced with an average backbone RMSD of 0.6 A, while the average backbone RMSD of the complete loop-helix-loop region (i.e., the helix with the surrounding loops, which are also repredicted) is 1.3 A.

Algorithms↗

PubMed2004

A kinematic view of loop closure.

We consider the problem of loop closure, i.e., of finding the ensemble of possible backbone structures of a chain segment of a protein molecule that is geometrically consistent with preceding and following parts of the chain whose structures are given. We reduce this problem of determining the loop conformations of six torsions to finding the real roots of a 16th degree polynomial in one variable, based on the robotics literature on the kinematics of the equivalent rotator linkage in the most general case of oblique rotators. We provide a simple intuitive view and derivation of the polynomial for the case in which each of the three pair of torsional axes has a common point. Our method generalizes previous work on analytical loop closure in that the torsion angles need not be consecutive, and any rigid intervening segments are allowed between the free torsions. Our approach also allows for a small degree of flexibility in the bond angles and the peptide torsion angles; this substantially enlarges the space of solvable configurations as is demonstrated by an application of the method to the modeling of cyclic pentapeptides. We give further applications to two important problems. First, we show that this analytical loop closure algorithm can be efficiently combined with an existing loop-construction algorithm to sample loops longer than three residues. Second, we show that Monte Carlo minimization is made severalfold more efficient by employing the local moves generated by the loop closure algorithm, when applied to the global minimization of an eight-residue loop. Our loop closure algorithm is freely available at http://dillgroup. ucsf.edu/loop_closure/.

Journal Article↗

PubMed2002

On the role of the crystal environment in determining protein side-chain conformations.

The role of crystal packing in determining the observed conformations of amino acid side-chains in protein crystals is investigated by (1) analysis of a database of proteins that have been crystallized in different unit cells (space group or unit cell dimensions) and (2) theoretical predictions of side-chain conformations with the crystal environment explicitly represented. Both of these approaches indicate that the crystal environment plays an important role in determining the conformations of polar side-chains on the surfaces of proteins. Inclusion of the crystal environment permits a more sensitive measurement of the achievable accuracy of side-chain prediction programs, when validating against structures obtained by X-ray crystallography. Our side-chain prediction program uses an all-atom force field and a Generalized Born model of solvation and is thus capable of modeling simple packing effects (i.e. van der Waals interactions), electrostatic effects, and desolvation, which are all important mechanisms by which the crystal environment impacts observed side-chain conformations. Our results are also relevant to the understanding of changes in side-chain conformation that may result from ligand docking and protein-protein association, insofar as the results reveal how side-chain conformations change in response to their local environment.

Computer Simulation↗