DeepPCM: Predicting Protein-Ligand Binding using Unsupervised Learned Representations

1 INTRODUCTION . A main goal of cheminformatics in the area of drug discovery is to model the interaction of small molecules with proteins in-silico . The ability to accurately predict the binding affinity of a ligand towards a biological target without the need to conduct expensive in-vitro experiments has the potential to accelerate the drug development process by enabling early prioritization of promising drug candidates ( Cortés-Ciriano et al. , 2015 ) . A common approach is to train a machine learning algorithm to predict the binding affinity of ligands towards a certain biological target using a training set of compounds that have been experimentally measured on this target . This modality is commonly referred to as a quantitative structure-activity-relationship ( QSAR ) model ( van Westen et al. , 2011 ) . QSAR models can be broadly classified into two types : single-task QSAR models and multi-task QSAR models ( Figure 1 ) . In single-task QSAR modeling , a model is trained separately for each protein to predict a binary or continuous outcome ( binding vs not-binding or the binding affinity ) given a compound input . The machine learning model used could be anything from logistic regression to deep neural networks ( Lenselink et al. , 2017 ; Cherkasov et al. , 2014 ) .In multi-task modeling , a single model is trained to predict binding across multiple proteins simultaneously , allowing the model to take advantage of the correlations in binding activity between compounds on different targets ( Caruana , 1997 ; Yuan et al. , 2016 ) . This is done , for example , by using a neural network with multiple output nodes where each output node corresponds to a different protein . Thus , multiple outputs are predicted given a compound input ( Simões et al. , 2018 ; Dahl et al. , 2014 ) . While these methods have been employed on various protein targets , there are methodological concerns to their use ( Lima et al. , 2016 ; Mitchell , 2014 ) . Both single-task and multi-task models must be retrained from scratch if one wishes to incorporate binding data for a new protein , and both can not be used at all to make predictions on new protein targets for which experimental data is absent ( van Westen et al. , 2011 ) . An attractive solution to this problem is to also include protein information in a so called proteochemometric model ( Figure 1 ) . The additional protein information enables a PCM model to directly utilize similarities between proteins for bioactivity modeling ( Cortés-Ciriano et al. , 2015 ; van Westen et al. , 2011 ) . This leads to many potential benefits over classical QSAR single-task and multi-task modeling . First , as proteins are explicitly represented by a defined featurization , PCM models can be used to make activity predictions on proteins without pre-existing bioactivity data , which is impossible with single-task or multi-task modeling . They can also be used to model binding on proteins for which experimental data may be too limited to train an effective single-task model ( van Westen et al. , 2011 ) . Second , with an expressive protein descriptor , a model could leverage similarities and differences between proteins directly to model their binding behaviors , rather than merely using correlations found among the compounds they bind to , as in multi-task modeling , or ignoring protein relationships altogether as in single-task modeling ( Cortés-Ciriano et al. , 2015 ; van Westen et al. , 2011 ) . Consequently , PCM models have found success on a variety of protein targets , and using many different machine learning methods , including random forests and SVMs ( Ballester & Mitchell , 2010 ; Weill et al. , 2011 ; van Westen et al. , 2013 ; Shiraishi et al. , 2013 ; Cheng et al. , 2012 ) . Recent advances in the field of Deep Learning have also led to its use in QSAR single and multitask modeling as well as PCM modeling ( Lenselink et al. , 2017 ; Menden et al. , 2013 ; LeCun et al. , 2015 ; Schmidhuber , 2015 ) . Lenselink et al . ( 2017 ) compared deep learning methods against other machine learning methods for PCM , single-task QSAR , and multi-task QSAR models on a benchmark dataset , and the authors found that PCM models using deep neural networks outperform other machine learning methods as well as single-task and multi-task QSAR models . All of the aforementioned works make features for both the small molecules and the proteins based on hand-crafted feature extraction protocols . Small molecules are often represented by counting and aggregating smaller substructures , while proteins are often represented by aggregation of computed physio-chemical features of their amino-acids or by encoding the differences in aligned sequences . In many application domains of Deep Learning , recent research has shown that these methods generally work better when the input data representation is lower-level and unabstracted , allowing the model to learn hierarchical features directly rather than relying on features which are hand-crafted by humans ( LeCun et al. , 2015 ; Schmidhuber , 2015 ) . For example , this is the case in computer vision , where deep learning on pixel value features has been the state of the art for several years ( Schmidhuber , 2015 ) . In cases where the input space is too high-dimensional , and especially if there is not enough labeled data to train a model end-to-end , unsupervised representation learning is used to generate lower-dimensional embeddings , again relying on machine learning , rather than human engineering , for feature extraction . This is the case in natural language processing , as well as in video analysis ( Srivastava et al. , 2015 ; Erhan et al. , 2010 ; Mikolov et al. , 2013 ) . In this work , we follow this reasoning and utilize such unsupervised-learned embeddings to represent both the ligand and protein spaces for proteochemometric modelling . To the best of our knowl- edge , we combine , for the first time , compound and protein representations both generated by deep learning models that were pretrained on an unsupervised learning task , as inputs to a PCM model . Moreover , we believe that this is the first work which combines unsupervised-learned embeddings of biological and chemical entities simultaneously in order to solve a downstream task . 1.1 COMPOUND DESCRIPTORS . The following section describes the currently used handcrafted ligand descriptors , evaluates their properties , and introduces the ligand descriptors that we use for our model — known as CDDD descriptors — which are generated via unsupervised machine learning . Handcrafted Compound Descriptors The state-of-the-art compound descriptors that are used for a vast majority of PCM and other chemoinformatics tasks are different varieties of circular fingerprints . In binary keyed circular fingerprints , each bit refers to a specific substructure 's presence or absence in the molecule , while in counts format the bits refer to the number of occurrences of the substructure ( van Westen et al. , 2011 ; Glen et al. , 2006 ; Rogers & Hahn , 2010 ) . As the number of potential substructures is vast ( ∼232 ) , the resulting sparse set of bits is usually hashed and folded to a much smaller size ( ∼103 ) at the expense of hash and bit collisions ( Rogers & Hahn , 2010 ) . These structure-based descriptors can be augmented with physicochemical descriptors , such as DRAGON or PaDEL , along with other chemical descriptors like atom identity/type , MACCs keys , and topological indices ( Mauri et al. , 2006 ; Yap , 2011 ) . Issues Circular fingerprints contain information about only the presence or absence of certain substructures in the compound – they thus fail to capture the shape or arrangement of those substructures within the compound . Furthermore , fingerprints rely on a hashing protocol to compress the millions of different substructures that are recorded into a smaller vector – as a result , hash collisions can mean that completely different substructures can correspond to the same fingerprint bit . This also means that one can not determine which exact substructures are responsible for the model prediction . CDDD – Ligand Descriptor To avoid these issues , we use the CDDD ( Continuous and Data Driven Molecular Descriptors ) , a model for the generation of lower-dimensional representation vectors of molecules developed by Winter et al . ( 2019 ) . This model uses a recurrent autoencoder trained on the task of translating non-canonical SMILES string representations of compounds into their canonical form . After unsupervised learning on approximately 72 million compound SMILES , a given compound is represented by the 512-length bottleneck of the translation model , and thus is encoded into a 512-length vector . These embeddings have been shown to be effective on QSAR prediction and virtual screening tasks ( Winter et al. , 2019 ) . CDDD descriptors offer a unique , compact , and continuous vector representation for each compound , as opposed to fingerprints , which are non-unique , discrete , and must be hashed to be made compact . Molecules with the same substructures but differently arranged will correspond to different vectors . Additionally , these unsupervised-learned descriptors have demonstrated competitive or superior results compared to molecular fingerprints on a variety of other tasks , indicating their ability to effectively represent compound properties and behaviors ( Winter et al. , 2019 ) . A diagram can be found in Appendix section D . 1.2 PROTEIN DESCRIPTORS . Next , we describe the various methodologies used for handcrafted protein descriptors . There are many different examples of highly specialized protein feature-extraction protocols used for PCM models that operate over a narrow range of targets . We describe the broad categories , examine their drawbacks and introduce the unsupervised protein descriptor which we use , known as UniRep . Handcrafted Protein Descriptors Most commonly used protein descriptors can be described as either amino-acid-based , or sequence-based . Amino-acid-based descriptors are computed using some combination of physicochemical and structural properties of individual amino acids . For example , the commonly used Z-scale descriptors and their counterpart ProtFP descriptors are constructed by computing various amino acid physicochemical properties ( such as hydrophobicity or polarity ) , taking a PCA over these properties , and then representing each amino acid by its first few principal components ( Sandberg et al. , 1998 ; van Westen et al. , 2013 ) . These descriptors can be combined with structural information descriptors , such as normalized van der Waals volume , charge , and the secondary structure of the protein at that amino acid residue ( Lapins et al. , 2013 ) . The whole protein is then represented by aggregating the amino-acid descriptors over the whole sequence . Sequence-based descriptors are constructed by taking aligned sequences or regions and using encodings to represent the amino acid identity at a given location in the aligned region . For example , in Nabu et al . ( 2015 ) , the authors build a model that predicts the bioactivity on mutated vs wild type penicillin binding proteins based on the positions of the mutations in the sequence . This was done by aligning the protein sequences and uniquely representing the positions a mutation may have occurred at with a one-hot-encoding . Thus , from 75 potential mutation sites , a 75-length vector is returned for each protein . Other variations of this technique use motifs or encodings of multiple varying segments ( Lapinsh et al. , 2005 ) . Additionally , on proteins for which 3D-information is available , this information can be incorporated into the protein descriptor . Methods vary – in general , 3D-models , for example crystal structures or water-field maps , are first aligned . Subsequently , descriptors are built based on amino acid identity at specific positions at the binding pocket or taking a PCA over the aligned field maps ( van Westen et al. , 2013 ; Lapinsh et al. , 2005 ; Subramanian et al. , 2016 ; Kruger & Overington , 2012 ) . Issues Amino acid-based descriptors require crude aggregation or binning methods , as they must convert variable length sequences of amino acids into a constant-length vector . For example , a common strategy is to divide the sequence into equal-length segments , then average over the descriptors for each segment ( Lenselink et al. , 2017 ; van Westen et al. , 2013 ) . This leads to the undesirable property where a single amino acid insertion early in the sequence would shift all subsequent segments , thus changing every descriptor bit despite a small change to the protein itself . Additionally , motifs and functional domains have variable numbers of amino acids which are not necessarily contiguous , so averaging over fixed-length contiguous segments can fail to capture relevant features consistently . Sequence and 3D-structure based descriptors must be aligned , which restricts the usable bioactivity data to only a very small fraction of closely related proteins for which an alignment is meaningful . These descriptors also can not be applied to new regions of the protein space , where bioactivity measurements of similar proteins are unavailable . Essentially , these methods build descriptors that are explicitly based on the differences between a few proteins , which greatly limits the scope of the problems that can be approached with such descriptors , as well as the amount of data that can be leveraged to train models . These data availability issues are exacerbated when using 3D-descriptors , since 3D structures are only available for a subset of proteins . UniRep – Protein Descriptor We use UniRep developed by Alley et al . ( 2019 ) , which uses a multiplicative LSTM architecture on approximately 24 million protein amino acid sequences taken from UniRef50 ( Krause et al. , 2016 ) . The UniRep model is trained on the next-character-prediction task , where the LSTM predicts the next amino acid in the protein sequence given the previous amino acids . To generate fixed-length embeddings , the hidden states of the model that are generated during the forward pass are averaged over the sequence dimension . For implementation details , refer to Alley et al . ( 2019 ) . The pre-trained model can be used out-of-the-box , taking as input amino acid sequences and outputting embeddings of length 64 , 256 , or 1900 depending on the architecture used . For the PCM model which we propose here , we found that the 256-length embeddings performed best , and so we will use UniRep to refer to the 256-length UniRep descriptor in this paper . A diagram can be found in Appendix section E UniRep descriptors offer many advantages over their handcrafted counterparts . The descriptors are alignment-free sequence-based representations , which therefore allow the utilization of protein bioactivity assay data across families and species instead of just highly similar proteins , greatly increasing the available training data . Moreover , these descriptors do not require 3D-structure information , which is available for only a small subset of proteins . Additionally , there is no need for binning or averaging across arbitrary length contiguous chunks of sequence .

Summary: The authors present a model with state-of-the-art performance for predicting protein-ligand affinity and provide a thorough set of benchmarks to illustrate the superiority of combining learned low-dimensional embedding representations  of both ligands and proteins. The authors then show that these learned representations are more powerful than handcrafted features such as circular fingerprints, etc. when combined into a model that jointly takes as input both the ligand and protein.