REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes

One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales. To overcome this bottleneck, we present the REinforcement learning based Adaptive samPling (REAP) algorithm that aims to efficiently sample conformational space by learning the relative importance of each reaction coordinate as it samples the landscape. To achieve this, the algorithm uses concepts from the field of reinforcement learning, a subset of machine learning, which rewards sampling along important degrees of freedom and disregards others that do not facilitate exploration or exploitation. We demonstrate the effectiveness of REAP by comparing the sampling to long continuous MD simulations and least-counts adaptive sampling on two model landscapes (L-shaped and circular), and realistic systems such as alanine dipeptide and Src kinase. In all four systems, the REAP algorithm consistently demonstrates its ability to explore conformational space faster than the other two methods when comparing the expected values of the landscape discovered for a given amount of time. The key advantage of REAP is on-the-fly estimation of the importance of collective variables, which makes it particularly useful for systems with limited structural information

Billion-years old proteins show the importance of N-lobe orientation in Imatinib-kinase selectivity

The molecular origins of proteins' functions are a combinatorial search problem in the proteins' sequence space, which requires enormous resources to solve. However, evolution has already solved this optimization problem for us, leaving behind suboptimal solutions along the way. Comparing suboptimal proteins along the evolutionary pathway, or ancestors, with more optimal modern proteins can lead us to the exact molecular origins of a particular function. In this paper, we study the long-standing question of the selectivity of Imatinib, an anti-cancer kinase inhibitor drug. We study two related kinases, Src and Abl, and four of their common ancestors, to which Imatinib has significantly different affinities. Our results show that the orientation of the N-lobe with respect to the C-lobe varies between the kinases along their evolutionary pathway and is consistent with Imatinib's inhibition constants as measured experimentally. The conformation of the DFG-motif (Asp-Phe-Gly) and the structure of the P-loop also seem to have different stable conformations along the evolutionary pathway, which is aligned with Imatinib's affinity

Rational design of additives for inhibition of protein aggregation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 189-197).Protein based therapeutics hold great promise in the treatment of human diseases and disorders and subsequently, they have become the fastest growing sector of new drugs being developed. Proteins are, however, inherently unstable and the degraded form can be quite harmful if administered to a patient. Of the various degradation pathways, aggregation is one of the most common and a cause for great concern. Aggregation suppressing additives have long been used to stabilize proteins, and they still remain the most viable option for combating this problem. However, the mechanisms by which the most commonly used additives inhibit aggregation still remain a mystery for the most part. It is clear that additive selection and the development of better performing additives will benefit from a more refined understanding of how commonly used additives inhibit or enhance aggregation. Aqueous arginine solutions are widely used to suppress protein aggregation and protein-protein interactions. Attempts have been made to develop cosolvents that are similar to arginine, but more effective at inhibiting aggregation. Therefore, a clear picture of the mechanism by which arginine inhibits protein aggregation is desirable. Baynes and Trout have proposed the design of a novel class of additives called "Neutral Crowder", which does not affect the free energy of isolated protein molecules but selectively increases the free energy of the protein-protein encounter complex. They proposed that arginine can be a "Neutral crowder" as the magnitude of the observed aggregation suppression effect of arginine is quantitatively equivalent to a neutral crowder of its size. On the basis of the results obtained in this thesis, we have been able to show that self-interaction of arginine plays a critical role in the mechanism by which it inhibits aggregation. The preferential interaction between protein and arginine is also influenced by the intrasolvent interactions in aqueous arginine solutions, something that is often overlooked and yet essential to understanding the effect of additives on aggregation. Furthermore, the linking together of arginine clusters into bigger clusters by hydrogen bond accepting counterions enhances its aggregation suppressing ability. According to the "Neutral Crowder" theory, large molecules that have the same concentration on the protein surface as the bulk solution should be effective at inhibiting protein association. However, large molecules naturally tend to be excluded from protein surfaces (e.g. polyethylene glycol) due to steric exclusion. We theorized, though, that if functional groups which tend to preferentially bind to proteins (e.g. guanidinium, urea, etc.) were added to the surface of a large, core structure that the resulting molecule could potentially behave as a neutral crowder. Therefore, creating a neutral crowder molecule requires a balance between attraction and repulsion with respect to the surface of a protein. Choosing a proper balance of interactions allowed us to produce compounds which have been shown to be potent aggregation suppressors, slowing aggregation by an order of magnitude more than the commonly used additives. Such potent aggregation suppressing additives might be useful during production and formulation, as they could improve yield and extend the shelf-life of protein therapeutics.by Diwakar Shukla.Ph.D

Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.Comment: 46 pages, 10 figure

Class(es) of Factor-Type Estimator(s) in Presence of Measurement Error

When data is collected via sample survey it is assumed whatever is reported by a respondent is correct. However, given the issues of prestige bias, personal respect and honor, respondents’ self-reported data often produces over- or under- estimated values as opposed to true values regarding the variables under question. This causes measurement error to be present in sample values. This article considers the factortype estimator as an estimation tool and examines its performance under a measurement error model. Expressions of optimization are derived and theoretical results are supported by numerical examples

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.Comment: 64 pages, 11 figure

Effects of Solute-Solute Interactions on Protein Stability Studied Using Various Counterions and Dendrimers

Much work has been performed on understanding the effects of additives on protein thermodynamics and degradation kinetics, in particular addressing the Hofmeister series and other broad empirical phenomena. Little attention, however, has been paid to the effect of additive-additive interactions on proteins. Our group and others have recently shown that such interactions can actually govern protein events, such as aggregation. Here we use dendrimers, which have the advantage that both size and surface chemical groups can be changed and therein studied independently. Dendrimers are a relatively new and broad class of materials which have been demonstrated useful in biological and therapeutic applications, such as drug delivery, perturbing amyloid formation, etc. Guanidinium modified dendrimers pose an interesting case given that guanidinium can form multiple attractive hydrogen bonds with either a protein surface or other components in solution, such as hydrogen bond accepting counterions. Here we present a study which shows that the behavior of such macromolecule species (modified PAMAM dendrimers) is governed by intra-solvent interactions. Attractive guanidinium-anion interactions seem to cause clustering in solution, which inhibits cooperative binding to the protein surface but at the same time, significantly suppresses nonnative aggregation.Singapore-MIT Allianc