Fragments and hot spots in drug discovery

R01 GM064700 - NIGMS NIH HHSPublished versio

Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes

Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 � ligand interface Ca root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods

Translation by Joseph Szanto of Kotlan, Sandor, Mocsy, Janos, and Vajda, Todor. 1929. A juhok coccidiosisa\u27nak okozo\u27i egy u\u27j faj kapcsa\u27n = [Coccidiosis of sheep in connection with a new species.] \u3ci\u3eAllatorvosi Lapok\u3c/i\u3e 52(23): 304-306

Translation number 4, College of Veterinary Medicine, University of Illinois, Urbana, Illinois, United States Translation by Joseph Szanto of Kotlan, Sandor, Mocsy, Janos, and Vajda, Todor. 1929. A juhok coccidiosisa\u27nak okozo\u27i egy u\u27j faj kapcsa\u27n = [Coccidiosis of sheep in connection with a new species.] Allatorvosi Lapok 52(23): 304-306 Translation from Hungarian to English by Joseph Szanto of University of Illinois, Urbana, Illinois, United States, July 7, 1960 (4 pages

How proteins bind macrocycles

The potential utility of synthetic macrocycles (MCs) as drugs, particularly against low-druggability targets such as protein-protein interactions, has been widely discussed. There is little information, however, to guide the design of MCs for good target protein-binding activity or bioavailability. To address this knowledge gap, we analyze the binding modes of a representative set of MC-protein complexes. The results, combined with consideration of the physicochemical properties of approved macrocyclic drugs, allow us to propose specific guidelines for the design of synthetic MC libraries with structural and physicochemical features likely to favor strong binding to protein targets as well as good bioavailability. We additionally provide evidence that large, natural product-derived MCs can bind targets that are not druggable by conventional, drug-like compounds, supporting the notion that natural product-inspired synthetic MCs can expand the number of proteins that are druggable by synthetic small molecules.R01 GM094551 - NIGMS NIH HHS; GM064700 - NIGMS NIH HHS; GM094551 - NIGMS NIH HHS; R01 GM064700 - NIGMS NIH HHS; GM094551-01S1 - NIGMS NIH HH

Efficient maintenance and update of nonbonded lists in macromolecular simulations

Molecular mechanics and dynamics simulations use distance based cutoff approximations for faster computation of pairwise van der Waals and electrostatic energy terms. These approximations traditionally use a precalculated and periodically updated list of interacting atom pairs, known as the “nonbonded neighborhood lists” or nblists, in order to reduce the overhead of finding atom pairs that are within distance cutoff. The size of nblists grows linearly with the number of atoms in the system and superlinearly with the distance cutoff, and as a result, they require significant amount of memory for large molecular systems. The high space usage leads to poor cache performance, which slows computation for large distance cutoffs. Also, the high cost of updates means that one cannot afford to keep the data structure always synchronized with the configuration of the molecules when efficiency is at stake. We propose a dynamic octree data structure for implicit maintenance of nblists using space linear in the number of atoms but independent of the distance cutoff. The list can be updated very efficiently as the coordinates of atoms change during the simulation. Unlike explicit nblists, a single octree works for all distance cutoffs. In addition, octree is a cache-friendly data structure, and hence, it is less prone to cache miss slowdowns on modern memory hierarchies than nblists. Octrees use almost 2 orders of magnitude less memory, which is crucial for simulation of large systems, and while they are comparable in performance to nblists when the distance cutoff is small, they outperform nblists for larger systems and large cutoffs. Our tests show that octree implementation is approximately 1.5 times faster in practical use case scenarios as compared to nblists

Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes

We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth “permissive” subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.Published versio

Analysis of Binding Site Hot Spots on the Surface of Ras GTPase

We have recently discovered an allosteric switch in Ras, bringing an additional level of complexity to this GTPase whose mutants are involved in nearly 30% of cancers. Upon activation of the allosteric switch, there is a shift in helix 3/loop 7 associated with a disorder to order transition in the active site. Here, we use a combination of multiple solvent crystal structures and computational solvent mapping (FTMap) to determine binding site hot spots in the “off” and “on” allosteric states of the GTP-bound form of H-Ras. Thirteen sites are revealed, expanding possible target sites for ligand binding well beyond the active site. Comparison of FTMaps for the H and K isoforms reveals essentially identical hot spots. Furthermore, using NMR measurements of spin relaxation, we determined that K-Ras exhibits global conformational dynamics very similar to those we previously reported for H-Ras. We thus hypothesize that the global conformational rearrangement serves as a mechanism for allosteric coupling between the effector interface and remote hot spots in all Ras isoforms. At least with respect to the binding sites involving the G domain, H-Ras is an excellent model for K-Ras and probably N-Ras as well. Ras has so far been elusive as a target for drug design. The present work identifies various unexplored hot spots throughout the entire surface of Ras, extending the focus from the disordered active site to well-ordered locations that should be easier to target

Improved prediction of MHC-peptide binding using protein language models

Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art

Discovery of macrocyclic inhibitors of apurinic/apyrimidinic endonuclease 1

Apurinic/apyrimidinic endonuclease 1 (APE1) is an essential base excision repair enzyme that is upregulated in a number of cancers, contributes to resistance of tumors treated with DNA-alkylating or -oxidizing agents, and has recently been identified as an important therapeutic target. In this work, we identified hot spots for binding of small organic molecules experimentally in high resolution crystal structures of APE1 and computationally through the use of FTMAP analysis (http://ftmap.bu.edu/). Guided by these hot spots, a library of drug-like macrocycles was docked and then screened for inhibition of APE1 endonuclease activity. In an iterative process, hot-spot-guided docking, characterization of inhibition of APE1 endonuclease, and cytotoxicity of cancer cells were used to design next generation macrocycles. To assess target selectivity in cells, selected macrocycles were analyzed for modulation of DNA damage. Taken together, our studies suggest that macrocycles represent a promising class of compounds for inhibition of APE1 in cancer cells.This work was supported by grants from the National Institutes of Health (Grant R01CA205166 to M.R.K. and M.M.G. and Grant R01CA167291 to M.R.K.) and by the Earl and Betty Herr Professor in Pediatric Oncology Research, Jeff Gordon Children's Foundation, and the Riley Children's Foundation (M.R.K.). Work at the BU-CMD (J.A.P., L.E.B., R.T.) is supported by the National Institutes of Health, Grant R24 GM111625. D.B. and S.V. were supported by the National Institutes of Health, Grant R35 GM118078. (R35 GM118078 - National Institutes of Health; R01CA205166 - National Institutes of Health; R01CA167291 - National Institutes of Health; R24 GM111625 - National Institutes of Health; Earl and Betty Herr Professor in Pediatric Oncology Research; Jeff Gordon Children's Foundation; Riley Children's Foundation)Accepted manuscriptSupporting documentatio

Identification of substrate binding sites in enzymes by computational solvent mapping,

Enzyme structures determined in organic solvents show that most organic molecules cluster in the active site, delineating the binding pocket. We have developed algorithms to perform solvent mapping computationally, rather than experimentally, by placing molecular probes (small molecules or functional groups) on a protein surface, and finding the regions with the most favorable binding free energy. The method then finds the consensus site that binds the highest number of different probes. The probe-protein interactions at this site are compared to the intermolecular interactions seen in the known complexes of the enzyme with various ligands (substrate analogs, products, and inhibitors). We have mapped thermolysin, for which experimental mapping results are also available, and six further enzymes that have no experimental mapping data, but whose binding sites are well characterized. With the exception of haloalkane dehalogenase, which binds very small substrates in a narrow channel, the consensus site found by the mapping is always a major subsite of the substrate-binding site. Furthermore, the probes at this location form hydrogen bonds and non-bonded interactions with the same residues that interact with the specific ligands of the enzyme. Thus, once the structure of an enzyme is known, computational solvent mapping can provide detailed and reliable information on its substrate-binding site. Calculations on ligand-bound and apo structures of enzymes show that the mapping results are not very sensitive to moderate variations in the protein coordinates