Generative artificial intelligence for small molecule drug design.

In recent years, the rapid advancement of generative artificial intelligence (GenAI) has revolutionized the landscape of drug design, offering innovative solutions to potentially expedite the discovery of novel therapeutics. GenAI encompasses algorithms and models that autonomously create new data, including text, images, and molecules, often mirroring characteristics of existing datasets. This comprehensive review delves into the realm of GenAI for drug design, emphasizing recent advancements and methodologies that have propelled the field forward. Specifically, we focus on three prominent paradigms: transformers, diffusion models, and reinforcement learning algorithms, which have been exceptionally impactful in the last few years. By synthesizing insights from a myriad of studies and developments, we elucidate the potential of these approaches in accelerating the drug discovery process. Through a detailed analysis, we explore the current state and future directions of GenAI in the context of drug design, highlighting its transformative impact on pharmaceutical research and development.

PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications.

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski's rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.

Characterization of the role of Kunitz-type protease inhibitor domain in dimerization of amyloid precursor protein.

A major difference between amyloid precursor protein (APP) isoforms (APP695 and APP751) is the existence of a Kunitz type protease inhibitor (KPI) domain which has a significant impact on the homo- and hetero-dimerization of APP isoforms. However, the exact molecular mechanisms of dimer formation remain elusive. To characterize the role of the KPI domain in APP dimerization, we performed a single molecule pull down (SiMPull) assay where homo-dimerization between tethered APP molecules and soluble APP molecules was highly preferred regardless of the type of APP isoforms, while hetero-dimerization between tethered APP751 molecules and soluble APP695 molecules was limited. We further investigated the domain level APP-APP interactions using coarse-grained models with the Martini force field. Though the model initial ternary complexes (KPI-E1, KPI-KPI, KPI-E2, E1-E1, E2-E2, and E1-E2) generated using HADDOCK (HD) and AlphaFold2 (AF2), the binding free energy profiles and the binding affinities of the domain combinations were investigated via the umbrella sampling with Martini force field. Additionally, membrane-bound microenvironments at the domain level were modeled. As a result, it was revealed that the KPI domain has a stronger attractive interaction with itself than the E1 and E2 domains, as reported elsewhere. Thus, the KPI domain of APP751 may form additional attractive interactions with E1, E2 and the KPI domain itself, whereas it is absent in APP695. In conclusion, we found that the APP751 homo-dimer formation is predominant than the homodimerization in APP695, which is facilitated by the presence of the KPI domain.

Cosolvent Impurities in SWCNT Nanochannel Confinement: Length Dependence of Water Dynamics Investigated with Atomistic Simulations.

The advent of nanotechnology has seen a growing interest in the nature of fluid flow and transport under nanoconfinement. The present study leverages fully atomistic molecular dynamics (MD) simulations to study the effect of nanochannel length and intrusion of molecules of the organic solvent, hexafluoro-2-propanol (HFIP), on the dynamical characteristics of water within it. Favorable interactions of HFIP with the nanochannels comprised of single-walled carbon nanotubes traps them over time scales greater than 100 ns, and confinement confers small but distinguishable spatial redistribution between neighboring HFIP pairs. Water molecules within the nanochannels show clear signatures of dynamical slowdown relative to bulk water even for pure systems. The presence of HFIP causes further rotational and translational slowdown in waters when the nanochannel dimension falls below a critical length of 30 Å. The enhanced slowdown in the presence of HFIP is quantified from characteristic relaxation parameters and diffusion coefficients in the absence and presence of HFIP. It is finally seen that the net flow of water between the ends of the nanochannel shows a decreasing dependence with nanochannel length only when the number of HFIP molecules is small. These results lend insights into devising ways of modulating solvent properties within nanochannels with cosolvent impurities.

Computer Simulations of Intrinsically Disordered Proteins.

The investigation of intrinsically disordered proteins (IDPs) is a new frontier in structural and molecular biology that requires a new paradigm to connect structural disorder to function. Molecular dynamics simulations and statistical thermodynamics potentially offer ideal tools for atomic-level characterizations and thermodynamic descriptions of this fascinating class of proteins that will complement experimental studies. However, IDPs display sensitivity to inaccuracies in the underlying molecular mechanics force fields. Thus, achieving an accurate structural characterization of IDPs via simulations is a challenge. It is also daunting to perform a configuration-space integration over heterogeneous structural ensembles sampled by IDPs to extract, in particular, protein configurational entropy. In this review, we summarize recent efforts devoted to the development of force fields and the critical evaluations of their performance when applied to IDPs. We also survey recent advances in computational methods for protein configurational entropy that aim to provide a thermodynamic link between structural disorder and protein activity.

Signatures of protein thermal denaturation and local hydrophobicity in domain specific hydration behavior: a comparative molecular dynamics study.

We investigate, using atomistic molecular dynamics simulations, the association of surface hydration accompanying local unfolding in the mesophilic protein Yfh1 under a series of thermal conditions spanning its cold and heat denaturation temperatures. The results are benchmarked against the thermally stable protein, Ubq, and behavior at the maximum stability temperature. Local unfolding in Yfh1, predominantly in the beta sheet regions, is in qualitative agreement with recent solution NMR studies; the corresponding Ubq unfolding is not observed. Interestingly, all domains, except for the beta sheet domains of Yfh1, show increased effective surface hydrophobicity with increase in temperature, as reflected by the density fluctuations of the hydration layer. Velocity autocorrelation functions (VACF) of oxygen atoms of water within the hydration layers and the corresponding vibrational density of states (VDOS) are used to characterize alteration in dynamical behavior accompanying the temperature dependent local unfolding. Enhanced caging effects accompanying transverse oscillations of the water molecules are found to occur with the increase in temperature preferentially for the beta sheet domains of Yfh1. Helical domains of both proteins exhibit similar trends in VDOS with changes in temperature. This work demonstrates the existence of key signatures of the local onset of protein thermal denaturation in solvent dynamical behavior.

The non-uniform early structural response of globular proteins to cold denaturing conditions: a case study with Yfh1.

The mechanism of cold denaturation in proteins is often incompletely understood due to limitations in accessing the denatured states at extremely low temperatures. Using atomistic molecular dynamics simulations, we have compared early (nanosecond timescale) structural and solvation properties of yeast frataxin (Yfh1) at its temperature of maximum stability, 292 K (Ts), and the experimentally observed temperature of complete unfolding, 268 K (Tc). Within the simulated timescales, discernible "global" level structural loss at Tc is correlated with a distinct increase in surface hydration. However, the hydration and the unfolding events do not occur uniformly over the entire protein surface, but are sensitive to local structural propensity and hydrophobicity. Calculated infrared absorption spectra in the amide-I region of the whole protein show a distinct red shift at Tc in comparison to Ts. Domain specific calculations of IR spectra indicate that the red shift primarily arises from the beta strands. This is commensurate with a marked increase in solvent accessible surface area per residue for the beta-sheets at Tc. Detailed analyses of structure and dynamics of hydration water around the hydrophobic residues of the beta-sheets show a more bulk water like behavior at Tc due to preferential disruption of the hydrophobic effects around these domains. Our results indicate that in this protein, the surface exposed beta-sheet domains are more susceptible to cold denaturing conditions, in qualitative agreement with solution NMR experimental results.

Cross dimerization of amyloid-ß and αsynuclein proteins in aqueous environment: a molecular dynamics simulations study.

Self-assembly of the intrinsically unstructured proteins, amyloid beta (Aß) and alpha synclein (αSyn), are associated with Alzheimer's Disease, and Parkinson's and Lewy Body Diseases, respectively. Importantly, pathological overlaps between these neurodegenerative diseases, and the possibilities of interactions between Aß and αSyn in biological milieu emerge from several recent clinical reports and in vitro studies. Nevertheless, there are very few molecular level studies that have probed the nature of spontaneous interactions between these two sequentially dissimilar proteins and key characteristics of the resulting cross complexes. In this study, we have used atomistic molecular dynamics simulations to probe the possibility of cross dimerization between αSyn1-95 and Aß1-42, and thereby gain insights into their plausible early assembly pathways in aqueous environment. Our analyses indicate a strong probability of association between the two sequences, with inter-protein attractive electrostatic interactions playing dominant roles. Principal component analysis revealed significant heterogeneity in the strength and nature of the associations in the key interaction modes. In most, the interactions of repeating Lys residues, mainly in the imperfect repeats 'KTKEGV' present in αSyn1-95 were found to be essential for cross interactions and formation of inter-protein salt bridges. Additionally, a hydrophobicity driven interaction mode devoid of salt bridges, where the non-amyloid component (NAC) region of αSyn1-95 came in contact with the hydrophobic core of Aß1-42 was observed. The existence of such hetero complexes, and therefore hetero assembly pathways may lead to polymorphic aggregates with variations in pathological attributes. Our results provide a perspective on development of therapeutic strategies for preventing pathogenic interactions between these proteins.

Effect of the A30P mutation on the structural dynamics of micelle-bound αSynuclein released in water: a molecular dynamics study.

Atomistic molecular dynamics simulation has been used to probe the effect of the A30P mutation on the structural dynamics of micelle-bound, helical αSynuclein when released in an aqueous environment. On the timescales simulated, the effect of the mutation on the secondary structure is restricted to local changes close to the mutation site in the N-terminal helical domain. The changes are transient, and all residues except Lys23 recover their initial structure. The local behavior due to the mutation gives rise to a global difference in the A30P mutant in the form of a permanent kink in the N-terminal helical domain.